{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55</td>\n",
       "      <td>073</td>\n",
       "      <td>002200</td>\n",
       "      <td>55073002200</td>\n",
       "      <td>22</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>271167646</td>\n",
       "      <td>207509</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-90.31665 44.94203, -90.31663 44.943...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>073</td>\n",
       "      <td>001800</td>\n",
       "      <td>55073001800</td>\n",
       "      <td>18</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>501461029</td>\n",
       "      <td>51047757</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>POLYGON ((-90.07946 44.77197, -90.07803 44.771...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>073</td>\n",
       "      <td>001900</td>\n",
       "      <td>55073001900</td>\n",
       "      <td>19</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>264984505</td>\n",
       "      <td>137317</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-90.07924 44.94534, -90.07766 44.945...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>073</td>\n",
       "      <td>001600</td>\n",
       "      <td>55073001600</td>\n",
       "      <td>16</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>612930088</td>\n",
       "      <td>2009406</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-89.49232 45.09283, -89.49229 45.097...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>073</td>\n",
       "      <td>001700</td>\n",
       "      <td>55073001700</td>\n",
       "      <td>17</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>403700826</td>\n",
       "      <td>4983132</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>POLYGON ((-89.49104 44.77264, -89.49104 44.772...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        55        073    002200  55073002200     22  Census Tract   G5020   \n",
       "1        55        073    001800  55073001800     18  Census Tract   G5020   \n",
       "2        55        073    001900  55073001900     19  Census Tract   G5020   \n",
       "3        55        073    001600  55073001600     16  Census Tract   G5020   \n",
       "4        55        073    001700  55073001700     17  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  271167646    207509  ...       37        0        0       37   \n",
       "1          S  501461029  51047757  ...        0        0        0        0   \n",
       "2          S  264984505    137317  ...        0        0        0        0   \n",
       "3          S  612930088   2009406  ...        0        0        0        0   \n",
       "4          S  403700826   4983132  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        2        0        0        2   \n",
       "1        0       15        0        0       15   \n",
       "2        0        6        0        0        6   \n",
       "3        0        1        0        0        1   \n",
       "4        0       15        0        0       15   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-90.31665 44.94203, -90.31663 44.943...  \n",
       "1  POLYGON ((-90.07946 44.77197, -90.07803 44.771...  \n",
       "2  POLYGON ((-90.07924 44.94534, -90.07766 44.945...  \n",
       "3  POLYGON ((-89.49232 45.09283, -89.49229 45.097...  \n",
       "4  POLYGON ((-89.49104 44.77264, -89.49104 44.772...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/wi_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "208d6842-f420-4205-859d-888e06316d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1542 popn tracts for WI\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"WI\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population GA voter-age population\n",
      "state pop= 10711908 VAP pct Hispanic is  0.09037630619125347\n"
     ]
    },
    {
     "data": {
      "image/png": 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eLsniqX+CTv0t1btJWVKztlR1pqr+TFV/mmIQQVXXAatFZLBXNBb4HJgOXO6VXQ5M8x5PByaKSGsRGYgbVJ/rdX+Vicgob7bWd33vMcb4hcNuOq8/yeKgcRZETFokbJGIyPuqeqyIlFG9y0gAVdVUNl7+CfCUiLTCJYH8Hi6oPSsi3wdW4XJ6oaqLRORZXLCpAq5V1ZB3nmuAx4C2wEzvxxjjt/FLmP4TWD0bDhgLB51S93uMqQfRFrZatbCwUIuKirJdDWMaR/HjLsliflsYfwccNtGSLJoGEZFiVS2M91pSK9u9dRs9/Mer6qr0VM8YkzGdB8Lg8XDanW5mljEZkMwOiT8B/heXATjsFSvwrQzWyxjTEJV7YNYf3eOT/hcGHud+jMmgZFok1wODVXVzpitjjEnBqtkw7ceweSkM/65LsmjdWKYRJBNIVgPbMl0RY0wDlZfBm7fD3H9Bp75w2Qtw4Nhs18q0IMkEkuXAOyLyX6A8Uqiqf81YrYwxydv+jdu58KgfwIm/htbtsl0j08IkE0hWeT+tvB9jTLbt2gKLXoAjr4Jug12SRdux0GRJMivbf9MYFTHGJEEVPp8GL//MZewdeLzLj2VBxGRRMrO2ugE/x6VxbxMpV9UTM1gvY0yssnUuQ+8XM6Dn4fCd/1iSRdMkJNO19RQwBTgD+CEufYml0DWmMYVD8Mh4KFsL426HUddC0DY4NU1DMv8Su6jqwyJyvarOAmaJyKxMV8wYg9utsH0vl2Tx9Duh0wDoemC2a2VMNckkbaz0fq8VkdNF5AiqZ901xqRbOASz/1k9yeKBJ1kQMU1SMi2S34lIR+CnwD+ADsCNGa2VMS3ZxiVuYWHJXDhwHBxkO0ibpi2ZWVszvIfbgBMyWx1jWriiR2Hmz6FVOzjnQfjWhbY63TR5dXZticj+IvKSiGwSkQ0iMk1E9m+MyhnT4nQ5AA4+A66dC4ddZEHE5IRkuraeBu4FzvGeTwQmA0dlqlLGtBiVu+GdPwAC435jSRZNTkpmsF1U9d+qWuX9PEmCvdGNMfXw9Qdw/zHwwd+hfLtbbGhMDkqmRfK2iNwCPIMLIBcB/xWRzgCquiWD9TOm+dmzHd64zc3GKhgA350O+x+f7VoZ02DJBJKLvN8/iCm/EhdYbLzEmPooWwefPA2jfwwn/AJa7ZvtGhmTkmRmbQ1sjIoY06zt3OySLI78H+h2ENzwme1YaJqNZGZtXSAi7b3HvxKRF7xFicaYuqjCwqlw70h45VbYtMyVWxAxzUgyg+2/VtUyETkWOAV4HPhnZqtlTDOwfS08cwk8f6XbcOoHs2xlummWkhkjCXm/TwfuV9VpInJb5qpkTDMQDsGjp7okiyf/Do66xpIsmmYrmX/Za0TkAeAk4I8i0prkWjLGtDxbV0GH3l6Sxb+4WVldDsh2rYzJqGQCwoXAq8B4Vd0KdAb+XyYrZUzOCYfgw3vgnpEwL5JkcawFEdMiJGyRiEgHVd2O28zqHa+sM27f9qJGqZ0xuWD95zD9x7Cm2CVYPPj0bNfImEZVW9fW07jNrIpx60X8SX9s/Ygx4FofM2+GNh3gvIdh2HmWH8u0OAkDiaqe4f22dSTGxFJ1AaPbYBh6Noy/A/btmu1aGZMVtXVtDa/tjao6P/3VMaaJq9gFb//eDaaPux0GHOt+jGnBauva+ovv8QhcF1eEAidmpEbGNFUr3oPpP4HSFXDkVXtbJca0cLV1bUU3sRKRj/3PjWlR9myD1ydB8WNQMBAuf8lSvRvjk+wKKctvbVqusvXw2bNw9E9gzC+g1T7ZrpExTUrWFhaKSFBEPhaRGd7zziLyuogs9X4X+I69VUSWicgSETnFVz5CRBZ4r90tYv0MJk12boI5D7jH3Q6CGxa4FeoWRIypobbB9n+wtyXSR0Tu9r+uqtel+NnXA4uBDt7zW4A3VfUOb/+TW4CbRWQIblfGoUAv4A0ROUhVQ8D9wNXAbOBlYDwwM8V6mZZMFRY87/ZNLy+DA8a6/Fg2I8uYhGrr2vIvOixOeFQDiEgfXO6u3wM3ecUTgDHe48dxiyBv9sqfUdVyYIWILANGisjXQAdV/cg75xPA2VggMQ21rQRm3ARLX4XehTDhHkuyaEwSahtsfzyDn3sX8HOgva+sh6qu9T57rYhE8mz3xrU4Ikq8skrvcWx5DSJyNa7lQr9+/dJQfdPshKrgsdNhxwY45Q9w1A/cFF9jTJ0aPR2piJwBbFDVYhEZk8xb4pTFrrT3l9csVH0QeBCgsLDQJg6YvUpXQsc+LjPvGXe5JIudbQ2uMfWRjcH2Y4CzvK6pZ4ATReRJYL2I9ATwfm/wji8B+vre3wf4xivvE6fcmLqFquCDu92GU/MecmUHnGBBxJgGSGaHxGOSKUuWqt6qqn1UdQBuEP0tVb0MmA5c7h12OTDNezwdmCgirUVkIDAImOt1g5WJyChvttZ3fe8xJrF1C+Hhk+D1X7vB9EPOynaNjMlpyXRt/QOITZcSryxVdwDPisj3gVXABQCqukhEngU+B6qAa70ZWwDXAI8BbXGD7DbQbmo391/wyi3QphOc/ygMPcdWpxuTotqm/44Gjga6ichNvpc6AGkZhVTVd/BS1KvqZmBsguN+j5vhFVteBAxLR11MMxdJZ9J9iMvQe8ofYN8u2a6VMc1CbS2SVkA77xj/7KrtwPmZrJQxaVOxE976nZuBdfLvYMAx7scYkza1Tf+dBcwSkcdUdWUj1smY9Fj+Dky/DrauhJE/sCSLxmRIMrO2HhKRTpEnIlIgIq9mrkrGpGj3Vpj2Y3hiAgTy4Hsz4bQ/WRAxJkOSGWzv6u3VDoCqlvoWCxrT9OzcCAtfgGNugDG3QH7bbNfImGYtmUASFpF+qroKQET6Y9mATVOzYwMsnAqjroGug1ySRRtMN6ZRJBNIfgm8LyKzvOfH4aUbMSbrVF2K91dudgPrg06GLgdYEDGmEdUZSFT1FW/b3VG4tCQ3quqmjNfMmLpsXQ0zboRlr0OfkS7JYpcDsl0rY1qcZHNthXApS9oAQ0QEVX03c9Uypg6RJIs7N8Gpf3Jb31qSRWOyos5AIiJX4fYO6QN8gmuZfITt2W6yYcsK6NTPJVk862639W1B/2zXypgWLZnpv9cDRwIrvX3bjwA2ZrRWxsQKVcH7f4N7j3JpTgD2H9Okg0jxylLufXsZxStLs10VYzIqma6tPaq6R0QQkdaq+oWIDM54zYyJWPsZTP8xrP0UDj4Dhp6d7RrVqXhlKZc+NJuKqjCt8gI8ddUoRvQvqPuNxuSgZAJJibcg8UXgdREpxdK1m8Yy50F49VZo2xkufAKGTMh2jZIye/lmKqrChBUqq8LMXr7ZAolptpKZtXWO9/A2EXkb6Ai8ktFaGRNJZ9JjKBx6IZzye9inc7ZrlbRR+3ehVV6Ayqow+XkBRu1v05FN8yWqidcWikgA+ExVm02G3cLCQi0qKqr7QJMd5Tvgrd+61Can1Ej4nFOKV5Yye/lmRu3fxVojJueJSLGqFsZ7rdYWiaqGReRT/8p2YzJm2Zvw0g2wbbXbMz3HkyyO6F9gAcS0CMmMkfQEFonIXGBnpFBVbVs5kx67S+HVX8InT0GXQS7JYv/R2a6VMSZJyQSS32S8FqZl27kJPp8Gx94Ex98M+W2yXSNjTD0kE0hOU9Wb/QUi8kdgVoLjjalb2XpY+DyMvnZvksUcGkxvSmwsxmRbMoFkHHBzTNmpccqMqZsqfDoZXrkVKnfDQeNdfiwLIg1i61VMU5BwZbuIXCMiC4DBIvKZ72cF8FnjVdE0G6Ur4clz4cVroNvB8MP3o0kWbRV4w8Rbr2JMY6utRfI0MBP4A3CLr7xMVbdktFam+QlVweNnwK4tcNqdUPh9CLjvMbn4rbqpdCfZehXTFNS2Z/s2YBtwceNVxzQ7m7+CggEuyeKEe93jTv2qHZJrq8CbUuAb0b+Ap64a1SSCmmm5kknaaEz9hSrh3TvhvlF7kywOPK5GEIG936qDQq3fqptK91dT604a0b+Aa0840IKIyZpk9yMxJnnffOKSLK5bAEPOhmHn1np4Xd+qi1eW8sL8Ep4rWk1VWLPeCrDuJGOqs0Bi0mv2P+HVX8C+XeGiJ+GQM5N6W6JV4JFupPLKMJFkPtnu/mrO3UlNZezH5BYLJCY9IulMen4LDrsYTvkdtE39RhTpRooEEaH27q/G0hzTnzSlsR+TWyyQmNSUl8Ebv4G81i7JYv+j3U+a+LuRgsEA54/ow3nD+9gNLgNybdKDaToskJiGW/oGzLgBtpXAqB9lJMlic+5Gamps7Mc0VK1p5JsjSyOfBru2uHGQTydD18Ew4R7oOzLbtWrR0jW2YWMkJpEGp5E3Jq5dW2DxDDju53Dcz1y3VhOX7htkU7rhpnNsozmO/ZjMa/R1JCLSV0TeFpHFIrJIRK73yjuLyOsistT7XeB7z60iskxElojIKb7yESKywHvtbpEc3ryiqStbBx/c7bqvuh4INy6AE3+Z0SCSrnUjkRvtX15bwqUPzY6e7+k5q/jOw3N4ek79ttpJdL501ztZTW1di2l5stEiqQJ+qqrzRaQ9UCwirwNXAG+q6h0icgsuLcvNIjIEmAgMBXoBb4jIQaoaAu4HrgZmAy8D43FpXUy6qMLHT7r9QkLlcPDpLj9WGmZk1Sad37Jjb7QvzC/hn7O+4vXP1wPw3tJNAFxyVM3Fksmczz8onY2ZTza2YbKt0VskqrpWVed7j8uAxUBvYALwuHfY48DZ3uMJwDOqWq6qK4BlwEgR6Ql0UNWP1A30POF7j0mH0q/h32e7xYX7DYMffhBNsphJxStLueuNL9P2Ldu/cj4YDPBc0epoEImYuXBtg84Xe+PORusgMiHhppMH25RdkxVZHSMRkQHAEcAcoIeqrgUXbESku3dYb1yLI6LEK6v0HseWx/ucq3EtF/r1S+5bZ4sXqoLHz4RdpXD6X2HE96JJFtMl3jhD7ALEQB1pU5Lhn/m1Zutunplbsyvr1GE9E9aptvPFHpet1oGNbZhsylogEZF2wFTgBlXdXsvwRrwXtJbymoWqDwIPgpu1Vf/atiDVkizeB50HQsc+KZ2ytoAR2wUUuwDx0N4dmXTmUADufXtZgwe3IzfaSLqVyqowIjC0V0cuOrIflxzVr9ZuqdhrSHTjTvd05aY0qG9MIlkJJCKSjwsiT6nqC17xehHp6bVGegIbvPISoK/v7X2Ab7zyPnHKTUOEKuH9u+DdP8G422HUNTDw2ymftq6AETvOMGr/LuQFA1RUhQFYvHY7S9aVcfuMRUnd4OtS240+UbdUffN8pat1YCvNTa7IxqwtAR4GFqvqX30vTQcu9x5fDkzzlU8UkdYiMhAYBMz1usHKRGSUd87v+t5j6mPNfHhwDLz9O5cba9j5aTt1optzonGGEf0LOH9En2hzMxRWZi5cG/ccdc2eSiRRttzYOhXs04pLH5rNU3NWURHSRp8VZbOxTK7IRovkGOA7wAIR+cQr+wVwB/CsiHwfWAVcAKCqi0TkWeBz3Iyva70ZWwDXAI8BbXGztWzGVn3Nvt8tLmzXAyZOhoNPS+vpE40Z1NYyOG94n2j3U35egFOH9WTe11tqnOOF+SXRsZRUUnr4WzX+Os1evpnyynC1Y4MBabRxD5uNZXJFowcSVX2f+OMbAGMTvOf3wO/jlBcBw9JXuxYkks6k1xFwxHdcd1bbTmn/mNoCRn3HGWYuXMupw3pGxzqeK1odHUsJBht2o43XfXTtCQcCsGRdWY1BtwsK+zbKgsbI65POGErprgpbsW6aNFvZ3tLs2Q5v/C/ktYHxf4B+o9xPBjVkzMD/nuKVpdExkjkrtrDwm20AVIXdbV6A80ckTuRY2020tjUhpbsqEPbO4MgLCOcOT23iQaQ+tY19pHtsxMZaTKbZDoktyZevuR0Lix+DQNC1SpqAulaC+2/2FVVhJs9ZxfPFJQQCggCBgDCsV8eE546Mo1z0wEc1VrGP2r8Led55/N1WxStLWbN1N/l5AQLigsjtE4al5QZc19hHusdGbKzFZJq1SFqCnZvhlVtgwbPQ7RC48AnoEzf3WkKZ6hpJ5ttyZKwgMh4SGRPBexwKK7e9tIjB+7WvtcURVmXStIU1jxOv3eFNQffXKS8gXDyyH+fWI3V9XX+rusY+0j02YmMtJtMskLQEe7bCl6/A8bfAt38Kea3q9fZMdo0kswdGZMwkOg03pIRjzlNRFeauN77khpMOqrFAMCBC2Gt9hcNa7TNmL99MVcgFqFBo77f1SJ1CYaVXp7b1CiJ1/a3qWmuS7rUolorfZJp1bTVX279x60JUXVqTGxbACbfWO4hAzZv91PklaUtKGG/Krf/ckW4vgN+fcyiTrx7NMYO6xp2t8f7STdWmAUdaBlcdO5C8gBAAWuVX/0YebxpybSlQ6pKubqREU5QbKt3nM8bPWiTNjSrMfxxe+7VbZHjImV6SxU71Oo2/e6baLoUB4fniEqpC6Wmd+L8tF+zTqtrCw0lnDOW26QupDCn5QWHy1aMB6Nt5H/K9+vhHeRTXkojcvP0tg9snDKN0VwUF+7SKvh4Z0I/3bb2h3+CT6UaywW/T3FggaU62LIfp18HX78GAb8OZf29QksV4N7rIjfWbrbuZPHdVwq6ohoylRG7o9769rNq3+Snz3EJAgIqQ8s9ZX/He0o3RsYtxQ3rw5uL1hHzRRESia0D854pMob34X7OjN/nJ/5P4Bu5PqVKf1CzJdCPZlramubFA0lyEquDxCbC7FM64C4Zf3uAki/FudJFukafnrCIgAqoEA8I3W3dTvLI0etNNNldVPLHf5nt0aANsi76+YfueamMXh/XtxJjB3fn1tIWEvKnAkZlo8VoGL8wviaZeqfDSyUP1lsukM4ay6JttKDCsV8caqVkif5+6kjrWFhhs8Ns0NxZIct2mpVAw0CVZPOd+97hj3CTISUt0o4us5wirIgEhDEyeu4qp80ui38L903Qj37RjA0yiRXax3+aXrCvjzS82EA4r+XkBLjqyH0vWL6pWrxH9C/jPxyXM+9qNi4QUps4v4f/OObRGy2Dq/JJq16lUD5rllWF++eKC6KzooLhjIgH1gVlf8dYXGwhrcjm3ErHBb9PcWCDJVVUV8P5f4d074eTfuiSLA45Ny6kT3ej8N13x7raRqbgvzC9hY1k5kYZBWKFgHzewP9WXymRPZZhfT1uIejdjf1CJfEbk8e0zFhEOu5bPacP2Y+bCtVwxegDt2+ZHxzqWrCtj/qqt1eofGYiPbRmcN7wPzxetjo65nOctLmyVF4hel3/QJaxubYmgBIOBat1oFSl2STV22ndb2W4yyQJJLiopdptNbfgcDr0ADr0wpdPFu8nEu9EV7NMq2q2VlxcAVUJhjW4WVekbrAjgVoYXryzl+eKSaoPikW6oisowk6YtJKxKXkBAJDqIf+7wPtHgUxVWXvzEJXZ+b+kmzj68F68sWhftpvKvqwzWsvp8RP8CJl89Ou7A+l1vfBndKXHvueDEg7vTrX1rNpSVV9sMS0RY4+vWq6/GvLHb4L7JNAskueaj++C1X0K7/eDiKTB4fNzDkr1RJXuT8XdrBQLCbWcOZfB+7attFuUPFpFptpF1GrEiK9LD6mXVDSnqnaGyKswnq0rjby4DTPOCiv/1yMr0yOrzyL4jG8rK6d6+dXRBYbwAOaJ/AacO61ktkByyX3u+2rSTNxavJxgQqkKxtVGembuKF7xuvfrcmBvrxh75N/DN1t02uG8yygJJrogkWew9wg2kj/sNtKmZFqR4ZSlT55dEp+gGxN1cI/uRxwaYZGcQVevWQindVRE9bup8l64k7N1sgwFh0hlDo6/7u44CQF5QuKCwL0N7dYxO7/XfpoPBAKtLdyX+U7B3MTq4IHLsoK7RxYjFK0u5+F+zoy0WgKfmrGLkgAJuPvWQuNdXuquCgLjurIBA1/atWbK+zK2IrxFEIBymRtbhZIN3Y8zaivwNKqvCBINCXjBAKGSD+yYzLJA0dXu2weuTIK8tnHoH9DvK/cQRu00tVE8LAtT4JlzbDKLIjbFgn1as2bq7xs3I/83av0BQ1QUaqLlOxD/IXryyFETwhxEBxhzUjddi9lTfr31r1u8oj3ZjBQACEh1DiWQFBnejrqyq2Qqa+3UpF/zzQ046pAcK1VoqsX+HU4f1ZM7yzdHpx355AQgE3N8iGAywZutunp6zKuHmW7EyOWsr8t/s09Vbo4G0KqScPKQ7h/Xt1GhdaTYe07JYIGnKlsyEGTfCjvVw9E/2tkoSiN2mNiKSFgT2pv6oqNybUiTewLo/SLhWCOQHhbGH9KBr+9bVPi/yevTzFMp2V0a7lxQ30O0/971vL2PN1t01ur2CAYkGIb97LxvB1PklTJ6ztwtteL9OFK8spSqs3D5jb66tUft3IRiM1x3l6uYPUs8Vl0TXk/j/DkC1Zo/gWipjD+nBD453a3MiKVuembsqmoYlmVZGbHD1L5BMRaLADq6FFUmPn0k2HtMyWSBpinZugpk3w8LnoftQmPiU69Kqg/+brggogoa1WlqQVnkBKirDhIEPlm1i3tdbqu3BEa9fHdzttCqk0emvL8wv4YrRA6KD74GARNO6A/zrveU8/MGK6AD8s0WrmeKtTI8mRAwGyAsIobCbTqxeYsXYWVgnD+kBwMaycgIBd03BgDB/1dZo/Soq9968l6wrSzqxcWVVmNtfWsSw3h0Z6ssg7B/bCQgcc2DXGnm8Zi/fTFVYvdle6mUj1qRaGZHzpPOm6w/sAdz05bBSbYZaptliy5bJAklTtGcbLH0dxvwCjr0x6fxYI/oXMOmModENoCKD4fFmKH2wbFON/7PHZr3NCwaoCu29MfkHx8srwzz43nLUmyJ71bEDeej9FdFgElYI+VoEVSHlgVlfsbsytHdRYSjMxJH96NWpbbUV8+KLAsGAsH/Xfbn4wY+i3UwCHN63E3O/3pvrK+ClgC9eWcok/wLFOijwack2Pi3Zu/CxVV6A4w/qVq0r79RhPWu0HPyz2FrlJ14fE9HQ8alkxXaZXTF6AIvWbq/W7ZeMVLqmbLFly2SBpKnYVgKfTYFjb3JpTW5cEHcwvTb+DaBiWxoRI/oXcMNJB8XdutZ/YwuFlYtG9kUgOvNpaGSlt9eiidzvw2Glfdt8rjp2YDS45Adl77d1z5uL1xNWd/MOeAkRI2MUd7y8OHqcPwSEw1otQEVen+cLIgBnfMvdLH/xnwXVjm2Iiqowr3++nrwATBzZj/at86LBqXX+3hXu/llsk84YyiVH9YvehCN/64h4XT7pvukmyls27+stcVPsx5Nq15QttmyZLJBkWzgMxY/C6/8LGoIhZ7tAUs8gAjU3gIr3Dbe2LVxjb2z+FCF5AYFCmHTGUKbMW1XtG7yI+3b+9ze+dIv4BG47y+2A/OsXFxDSvavEFde6ObR3R4b2dtf49JxV/PPd5XGvya0s12o7FRLzGOCT1Vu54+XFPDN3FelSFXZBdMq81dHgVFHpFl+u2rLL1/WnzFy4FiA6Cy0YgIuO3LuPSaK0M+m+6SbKW5Zsa6e2VlKilkpseWMvtjTZZ4EkmzZ/5ZIsrnwfBh7vkix2Htjg0xXs0yruyvKIRN82/TcCf9dY6a6KvYEppDw1ZxVt8gMcN6gb/hxYYw/pwcJvtkW7nkIKby/ZwOF9O/Hbsw+NZt297SWX3gSBRd9sY8GabTxftJp2bRL/MxRcV9P4oftFFyXGs3LzroTBKCIYgDhLWqKfE68dM2/FluqtIYHnilZX62YLq0th/+FXm6NdalVhN+X4ueISzh/Rh13lVdHjY3OU1XZTbmg3U0NbO7Wlx0n0b6elDa77ZzPW1pXZklggyZZQFTxxthsPOeseOOKyWmdk+cW7uRSvLGXKvOrfxqfMW1WtSyPet80l68qi3Tb5wb2ry+es2MKYg7pF11ZElFeGWb99D3lBIRRSgkFX51lLNlT77Le+2MAbn6+PLhIcvF97wmFvh0Pde86KkLJlZ2XCa+1V0JbdFVW89GnNIBIMQLd2rVm3vTzh4kUBDujejs775FNUy/4pSvxgsnV39bqpF1T974v8jjcuU1EVrrG9b1VYq+UoizdTLpI+JtoiDAY4f0QfhvXqmNTNq6FdTMmkx/G3VBp7cD3bU4tjp9gHhBYTQGtjgaSxbVwCnQ9wSRbPfcAlWezQM+m3Pz1nVTStiD8j7aUPzWZPZfWv25+WbOPSh2ZH/5HHfttcur6s2rf8ipASWdURGSeInUeqwGcl2wgGhQO6t2PFph011nwIbmwjkt5k0rSFnHBwd+Is7ajTmtLdCV8LhaG8KlTr+xVYtmFHUp+V2shK8qJB1MtRFrkxxiaQnDJvVbWuykhAinfzSvQtuaGp8GOPi/zbqagMIyLR1m5jDq43hdZP7BR7m53mWCBpLFXl8N5f3M+438LoH0H/o+t1isiMpGh/fVX1rWHj8f8j93/bLNtdGbcrKOi1NCLjGfHurpGpwMneoKvCWi1PVTqV7qpK+zkP79ORz9ZsI8Ux+zqpulZjWIm2QCIZAhRYULKNvDgbeEXWAUX+u0a+XIS84C0QnRQQaXGmI2PxpDOGRs/lX7eTycF1fwskXa2fdMxKi0w4iUwaaemz0yyQNIbV81ySxY1fwLcmwmETG3Sa2cs3V+s+CXibOAF7dzAMuqmrs77c6FZeB4RPV2/lF/9ZUG1R4E+f/STuZ1xU2JeNZeU1Whn10Vjf7DOlZOtuTjqkBx8t30zZnroDVWz3X7Jci809rvBaIP5FlGGgW7tWDO3VkXeWbKiWSiaMGwOL/XIROW8kI/Mzc1elnLHYv7Yo3qLL+gyu1+cmHq+rL9XWTzLjhMl2GdoYyV4WSDLtw3+4bW879IZLn4dB4xp8qlH7d6F1vvs2FPAlKARq/OM+YXB3Fn6zjeeKVkeDwvNFq7nymIE1ptNGRLLu/uo/Cxpcx+Zg046KegXS7u1as66sPKXPDEO1mXARa7buYWNZOWMGd2f5pp3VWoGLvtlG6a6KGmMzAdy35A1l5dV3j4Skb77+rjL/zL1UcnbVt2sq3i6XqbZ+4rVqoH4LQ21WWk0WSDIlHHY7FPYZCYVXwkm3QZsOKZ2ytm6EeCulzxvep9o33IqQJpzZdGD3dlx5zED+/dHXLF5XllI9W5pUg0hdKkIaN7C9v3QTg/drXy3Z5Ij+BQzq0Z5zh/fhgVlfVTu+Z8c2QN2tAv8NPyAS7TKLrC3q3altg2aV1bdrKt74S6o38XjntNX4qbNAkm67t7o07/n7wGl/rjXJYkPU9n+k2MHaDWXl5HuZd+vy1YYd3PbSoqSONU3Dyi27WLnFZUkW73+KVpby2ZptnDu8T40uxpKte7jogQ9dRmN1ySdPPNjlTvN3e/r/HYFLR6PqUr/E5kyrzzf5+g7MZ2L8JdE5bTV+aiyQpNPiGfDfn8LOjXDM9XUmWUxV7LfBUft3IS8gVHh96W99sZ6xB/dg0drttc5+AqIztUxuci0G97iyKszU+SVs2L6nxnH+/8RV4b0JLKfMXcVvzz6US47qV+OGH7uLZWQGWH2/yScbGOItcEyn2HPaavzUWSBJhx0b4eWfwecvwn6HwiVToNfhaf0I//+5gGp7jvi3rPXnoAqFSWnQ3OSmkFLvFf4hJbrdQGzOtkjql0i246qwNnjwu67AkK0pvjbukRoLJOlQvh2Wvw0n/tq1RIL5aTlt7IBneaVbFR6AaM4qqL4PerJZb03zFhl/D4hLR1NRFa5z7Mu/3YA/T1fkuX+fm3QNfsey8YrcZIGkobauhs+egW//zEuyuAhat6/3aeItJAO318WUeasIhV3vWHRijkK8JXjJZrs1TUdQIM6WKbVKlMolkbxggMVrt8fdoMt/TqH69sj+m/nMhWurLcITSNvgdyzLHpybcj6QiMh44O9AEHhIVe/I6AeGw1D0MLxxG2gYhp5L8Y7O3reyqnrNp/cHi8j/SQO4qaB+1spofgS3pXAoyXEpAX5w3P60b5ufcDFprJEDCjiwR/u43Vz5Qbe7ZOwYSLzB51OH9Yxmiw56qVr8g+7pZOMVuSmnA4mIBIF7gXFACTBPRKar6ufp/qxDfjWTnqES/tTqIQrlC9j/BDjz7zz9pTBp2kf1WjkcWYkcby2HDXe3DOOG9OCNxcmPX4lA+7b50W0B+nXZlynzVrFo7fboRl8K0Y3Ezj68F3dNPCL6hSWy2dnQXh256Mh+cfeqiYh3M6/t+HSz8Yrck9OBBBgJLFPV5QAi8gwwAUhrIDnkVzOpqKrkidZ30J5d3Br6IX/4zh0Ur9rKpGkf1UhZUtdgYqIgYpqn2O6osw/vxXdGD+DdpRur7UIZ0a1dKzbuqL7dcKuYbp5LjupXbf8T/54y/pt9MmuP4ok3s8lu7iaRXA8kvYHVvuclQI1FGyJyNXA1QL9+/er9IburwkCQGyp+xErtwUYK+INIrSlLEol9j8kOIWbsKYXzgOsqap0X4Ih+BXyzdTd7KkP07NSWg7zFga8vWscri9Yxfuh+3HLaIUD1bATvLNnA+u17oq2Fi/81m8qqMHlB4YLCvgm7kuLd8Os6xph0y/VAEm+RRo1bg6o+CDwIUFhYWO9bR9u8ALurwhTpwdHnUHvKkkT873H7qqd+M2su8gJucNg/O6h1UAjjJhP4/075QRCk2iByu1ZB2rQK0mmfVvTq2IaFa7YRCAjD+xUwxksZIxDdbApcC3Hq/BIEaN86L7o17eD92vPC/BI2lpVHF+wB1SZG1CfX0oj+BdEA4i+LvO+So6p/wZn8PzZOYHKHaA6P5IrIaOA2VT3Fe34rgKr+IdF7CgsLtaioqN6fdcivZrK7KkzbvACLf3dqtLwhmUTjrQlZtr6M5Zt2Eg4rFxb2Zd32Pfx3wVqqQm7spWfHNt5GU0q71kFKd1WSHxC6tGvFtt2V7KwI0SYvQJv8IK28QHf24b35fO12Pli2ifxggJEDO7NtdyU9OrThB8cfwJJ1ZdFU5ZVhJT8gVIaVzvvkc2CP9tVunvFSYkTe371DG04Y3L3aTTXZ3fRS3V8i2/tTGNNSiEixqhbGfS3HA0ke8CUwFlgDzAMuUdVFid7T0EBijDEtWW2BJKe7tlS1SkR+DLyKm/77SG1BxBhjTPrldCABUNWXgZezXQ9jjGmpAtmugDHGmNxmgcQYY0xKLJAYY4xJiQUSY4wxKcnp6b8NISIbgZUNfHtXYFMaq5ML7JpbBrvmliGVa+6vqt3ivdDiAkkqRKQo0Tzq5squuWWwa24ZMnXN1rVljDEmJRZIjDHGpMQCSf08mO0KZIFdc8tg19wyZOSabYzEGGNMSqxFYowxJiUWSIwxxqTEAkmSRGS8iCwRkWUicku269NQItJXRN4WkcUiskhErvfKO4vI6yKy1Ptd4HvPrd51LxGRU3zlI0Rkgffa3SISb6OxJkNEgiLysYjM8J4362sWkU4i8ryIfOH99x7dAq75Ru/f9UIRmSwibZrbNYvIIyKyQUQW+srSdo0i0lpEpnjlc0RkQJ2VUlX7qeMHl6L+K2B/oBXwKTAk2/Vq4LX0BIZ7j9vj9nMZAvwJuMUrvwX4o/d4iHe9rYGB3t8h6L02FxiN26lyJnBqtq+vjmu/CXgamOE9b9bXDDwOXOU9bgV0as7XjNt6ewXQ1nv+LHBFc7tm4DhgOLDQV5a2awR+BPzTezwRmFJnnbL9R8mFH++P/arv+a3ArdmuV5qubRowDlgC9PTKegJL4l0rbu+X0d4xX/jKLwYeyPb11HKdfYA3gRPZG0ia7TUDHbybqsSUN+dr7g2sBjrjtsiYAZzcHK8ZGBATSNJ2jZFjvMd5uJXwUlt9rGsrOZF/oBElXllO85qsRwBzgB6quhbA+93dOyzRtff2HseWN1V3AT8Hwr6y5nzN+wMbgUe97ryHRGRfmvE1q+oa4E5gFbAW2Kaqr9GMr9knndcYfY+qVgHbgC61fbgFkuTE6x/N6XnTItIOmArcoKrbazs0TpnWUt7kiMgZwAZVLU72LXHKcuqacd8khwP3q+oRwE5cl0ciOX/N3rjABFwXTi9gXxG5rLa3xCnLqWtOQkOusd7Xb4EkOSVAX9/zPsA3WapLykQkHxdEnlLVF7zi9SLS03u9J7DBK0907SXe49jypugY4CwR+Rp4BjhRRJ6keV9zCVCiqnO858/jAktzvuaTgBWqulFVK4EXgKNp3tcckc5rjL5HRPKAjsCW2j7cAkly5gGDRGSgiLTCDUBNz3KdGsSbmfEwsFhV/+p7aTpwuff4ctzYSaR8ojeTYyAwCJjrNZ/LRGSUd87v+t7TpKjqraraR1UH4P7bvaWql9G8r3kdsFpEBntFY4HPacbXjOvSGiUi+3h1HQsspnlfc0Q6r9F/rvNx/3+pvUWW7UGjXPkBTsPNcPoK+GW265PCdRyLa6Z+Bnzi/ZyG6wN9E1jq/e7se88vvetegm/2ClAILPReu4c6BuSawg8whr2D7c36moHDgSLvv/WLQEELuObfAF949f03brZSs7pmYDJuDKgS13r4fjqvEWgDPAcsw83s2r+uOlmKFGOMMSmxri1jjDEpsUBijDEmJRZIjDHGpMQCiTHGmJRYIDHGGJMSCySmSfAy1f4ojecbIyJHxykfICIlIhKIKf9EREYmONfhInJamup1joioiBycjvN557xNRNZ417BQRM5K17m9848RL2NyLcdU+xuJyFmSw1myTf1YIDFNRSdc1tEaRCTYgPONwa1qrkZVv8blEfq27/wHA+1VdW6Ccx2OW2uTNG9FcDwXA+/jFkam099U9XDgAuCR2EDZCA7H9zdS1emqekcj18FkiQUS01TcARzgfav+s/ct+G0ReRpYACAiL4pIsbj9Jq6OvFHcXjHzReRTEXnTS0b5Q+BG73zfjvmsyVS/kU8EIntXPOrt0fCxiJzgZTK4HbjIO9dFIrKvuD0h5nnHTfDqcYWIPCciLwGvxV6gl9/sGNwCsom+8oCI3Odd1wwReVlEzvdeGyEis7zrfjWSBiMRVV0MVAFdReRi71oWisgffZ+3Q0T+4v3N3hSRbl75OyJS6D3uKi6lTOw1jBSRD73r/lBEBif4G10hIvd47+nvfc5n3u9+Xvlj4vbB+FBElkeu2eSgbK/StB/7UY2bFnsMLtHgQF9ZZ+93W9yK3C5AN1wLY2DMMbcBP0vwWfvhVgbnec8XA8OAnwKPemUH41JutMHtaXGP7/3/B1zmPe6Ey3iwr3dcCb5VxTGfexnwsPf4Q/buC3M+8DLui91+QKlXlu8d18077iLgkTjnjV4rcBQuZ1Jvr/7dcAkc3wLO9o5R4FLv8aTItQHvAIXe467A177/FpFsAB18f7eTgKne49i/0RW+874EXO49vhJ40Xv8GG4FdQC3b8aybP87tJ+G/SRqfhvTFMxV1RW+59eJyDne4764vEHdgHcjx6lqrcnlvGPWicgiYKyIrAcqVXWhiPwW+Id3zBcishI4KM4pTsYlgfyZ97wN0M97/HotdbgYl84eXPLIi4H5uLQ1z6lqGFgnIm97xwzGBbjXXTokgrgAGM+N4jLdluECTiHwjqpuBBCRp3AbIr2IS6U/xXvfk7jkhsnqCDwuIoNwASk/ifeMBs71Hv8btwlTxIvedX8uIj3qUQ/ThFggMU3ZzsgDERmD+wY8WlV3icg7uBu40LAU35HurfXeY4ifPjseAc5T1SXVCkWO8tc55rUuuE21homI4oKCisjPa/lcARap6ugk6vQ3Vb3T93lnJ/GeiMjfr4q93d1tEhz7W+BtVT3H60J8px6fE/t5AOW+x01mO1tTPzZGYpqKMtzWv4l0BEq9IHIwMMor/wg4XlxmU0Skc5Lnm4obHL4I1zoAeBe41DvPQbhWxpI453oV+IlIdI/rI5K4vvOBJ1S1v6oOUNW+uB0Mj8UNvp/njZX0wHUl4X12NxEZ7X1OvogMTeKzwG1Wdrw31hHEtX5mea8FvPoAXOJ9PsDXwAhffePpCKzxHl/hK6/t7/0he8eELvV9nmkmLJCYJkFVNwMfeAPDf45zyCtAnoh8hvtWPNt730bgauAFEfmUvV02LwHnJBhsR1W3eudY7+s+uw8IisgC7zxXqGo58DYwJDKQ7H1+PvCZiCz0ntflYuA/MWVTcTfyqbixlYXAA7ggsE1VK3A39D961/YJcWaixaMuTfitXt0/BearaiRN+E5gqIgU41pJt3vldwLXiMiHuDGSeP4E/EFEPsC1qiJi/0Z+1wHf8/7bfQe4PplrMLnDsv8a0wSISDtV3eF1gc0FjlG3p0gmPmuHqrbLxLlNy2RjJMY0DTNEpBPQCvhtpoKIMZlgLRJjjDEpsTESY4wxKbFAYowxJiUWSIwxxqTEAokxxpiUWCAxxhiTkv8P/qBfpGM/iNkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP GA\n",
      "total state pop= 10711908 , VAP pct Black is  0.3027172816867175\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for WI\n"
     ]
    },
    {
     "data": {
      "image/png": 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ASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for WA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n"
     ]
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - select ones for WA)\n",
    "tractFixList = [1056,1399]\n",
    "for tFL in range(len(tractFixList)):\n",
    "    m = tractFixList[tFL]\n",
    "\n",
    "#for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11, 22, 265, 346, 432, 736, 976, 1012, 1016, 1132, 1180, 1246, 1322]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 50\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 48 and x > -96 : #leftover from another state\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y > 40: #don't worry about Atlantic ocean tracts\n",
    "            if m==761 or m ==676 or m==825: #trust me on this for Wisconsin\n",
    "                    thisTract = \"interior\"\n",
    "            else:\n",
    "                if lakeTracts == [-99999]:\n",
    "                    lakeTracts = [m]\n",
    "                    isSkippedTract[m] = 1\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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16F8UVBZ0t20/NO8h7px6p1/j6snpcbKndg8FlQUUVBawrWpbd5NKdnQ205OmMzZ2LOdmnMu0pGlD3oTi8DhOPh/QucOq7ajtLqdQZERldA8K63liOCMyQ84HjBYVO42RoUfXGdMAXPo4TLhs8O3wXi8tDYWUVW6nom4fZQ1HKWstp6KjjjJ3C+W4qTYp9Amvl+j2kGGJIM0aQ0Z4MulRmaTHjyM9aRJ3ffYjrh9/Pd+f//0AfODgkkTfyau9vFf4Hh8c+4B3C9/lwuwL+d2S3/ncRmvN4YbDPLL+EZqcTfz7un/7Pa7+uL1u9tfvZ0vFFgoqC9hft5+yVmOS0NyYXGYkz2BywmTmpc1jQvyEQU3hECwtzhZjqojGopOOBlpcLd3lrCYr2dHZ3U1AMj4gBDVXwkePG/O8hMfBhT80Fsg+YXCi1+2kpnoXZVW7qKg7SFnzMcpbKyh3NlDuaaNceWk+oTZu1Zo0bSLDHEGaLY6MyFTSY3JIT5hAetI00pInE+ZjtPmCFxdww4Qb+O5Z3w3EJw+qUduP/kQmZeLyvMu5PO9yzCYzbx95mzVFa7h4TF9zsRmUUtjMNnbU7ACgorWCtMi0oMRrMVmYmjiVqYlT+fLULwPQ7GzmnaPvsLZ4LRvKNvDm4TcBSAlP4bK8y7gs9zImJ0zGZrbh9ropbynH6XWSEZVBuCV4J7iibFHdsfektaauo+7kUcJNRf2OD+h5yY7OJik8icTwxKB+HnEaXO3GnOuf/gbtdtA05w4qxi+msrmUig+/R0VzKZXtNZS7mij3dlBhAvcJO/YYryYdCxnWWObaE8iIzCAtLpeMhEmkp04nMS7vjCo4Gj2sKkiBMqoSfZfdtbupazcWKv79tt/7TPQA3/rwWwDcOulWEuwJAY/Pl2hbNDdNvImbJt4EQGVrJRvLN/LBsQ94ad9LvLDnBczKTFxYHE3Opu4TqQrFgvQFLM1bypzUOcSFxRFtiw76j1wpRWJ4IonhicxJndPrOY/XQ0VbRa/zAIVNheyq2cV7Re+dtEhMhCXCeC17Yt/X4Ykk2Y2dwlCPgA51ra5WKlorqGitoLKtkoqjH1Fx4C0qLGYqkqOpsMTRXr8WPl/bvY1Za5K9kG4KY0ZYMpeFJ5ERnUVa7FgykqeQljqDqIjkgMbdNQgy1I2qRO/VXn6z5Tc8v+d5Iq2R3DnlTm6dPPCw6rTINMpay/jq9K8Ou0FGqZGpXJN/DdfkX0Ojo5GN5Rs5WH+QmvYaYsNiyY3JJcwcxqGGQ6w6uoqH1z/cva3FZCE1IpXs6Gxmp8zmrql39UqIba42NpRtwOl1Em2LZlzsONKj0gP2WcwmM5lRmWRGZfY7PqCkpYTa9lpqO2qN687bhY2FbKnc0msa6Z7CLeEk2BN87xg6r6OsUaPin3+wOtwdRhJv60zkrZXdt7vu95yuA4xFKpLC7aS5PeTb4lgYFkdaRBppMTmkJeSTmjSV5OSpmK1DO4mfFy9mFfrniEZVon/7yNv8dfdfWT5+OQ/Ne4goW1Sf5VpdrbxX+B51HXU0Ohopby2n3d3O7prdpOT0PYXCcBAbFstluUbzTV++Ofub7Knbw5GGIzQ4Gqhpr+nuUfOH7X9gxZ4VTE6cTEpECq2uVjaVb6LN3dbrNZLDk8mNzWVa4jRmpsxkZvJMksKTAv7ZbGYbY+PGMjZurM9yLq+L+o763juDjlpq2mu6bxc3F7O9ejv1HfW9eg11v5fJdtKRQV9HC4n2RGJsMSN6p+D0OI0aeM/a+AnJvK+dZ4I9obuScFbaWaRFphmJPDKN1MhUUuzJWE0Wv03YFShe7UUxcv9+gzWqEv3HJR8TbY3mkXMe8fnP+fS2p3l+z/OA8U8fb4/n1km3sjBzYbBCDQilVJ/t5mCMI3j94OscbTzK1qqthJnDuCz3Mq4adxUJ9gSanE1sqdxCUVMRhxsOs2LvCp7b/RwAmVGZzEw2kv7MlJlMiJ8wZCtvWU1WUiJS+p3TqCe3102Do6HXkcGJ1xWtFeyu3U19R32vqaZ7vt9gjxRiw2KD2lTm9rqpbqvuXfvukdQrWit69Y7qEhsWS2pEKmmRacxImmEk8a5LRBopkSkh013Wq72jotfXqEr0Y+PG8m7huzyz4xnum3lfv+W6Jhh7YM4D3DPtnhFdYxusWSmzmJUyy2eZnnPuODwO9tbuZXv1drZXb6egooBVR1cBEGYOY2LCRKYlTmNa0jTmps712xgEf7KYLCSFJw3qiMSrvcd3Cic0G/U8athft5+6jrru8RC93k9ZiLfHk2BPINoW3esSZY0i2hZNjC2GKJtxO9ra+ZwtimhrdK8ptD1eT/eOqPvS1rsmXtNec9J5jShrVHcSn5QwidTI1O6aeFpkGqkRqaPmfEZXj8PRUKMP2e6VLq+LbVXb2FC2gX11+zjSeITSltLu59+89k3yYvP63FZrzTc//CbrStZxRe4V3D39biYlTDqjeEKd1pqK1gq2V29nZ81OdtXsYm/d3u55huxmOxqN3WJnUvwkrhx7JdfmXxuSO1Gv9tLkaOrzCKG2vZb6jnqaXc00O5tpcbYY166WPpuRegq3hBNljcJsMlPTVnPSziTcEt6dxLuue9bE0yLT+m2uHK1mPj+Te6bdw7fmfGuoQzljo6p7pdvrZuX+lfxxxx+p66jDoiyMixvHjKQZXD3uahLsCaREpJAT3f9kQkopnrjwCZ784kme3/M85a3lvLD0hSB+ipFHKUV6VDrpUelcnnc5YNQ6DzceZlP5JipbKzEpk9H2X7GJh9c/TGlLKV+f+fWQW1DFpEzE2eOIs8cxLm7coLbxai+trlaanc3dlxZXS5/3XV4XKREpvWriaZFpI/58wVCwKEufR1+hJqT+ww43HOa7H3+Xg/UHmZ82n9sm3cbZGWef1vKAjY5GHB4HADdPGiUr1PuZ2WRmQvwEJsRP6PW4V3v56Wc/5Y87/sjrB19nVsosJsRPYHzceOalzRuVa+qalKm7GUf0prWmqq2K0pZS8mLz/DoPlMVkwe2VRD9ieLWXa9+4FoD/vvC/uTjnYp+1mxf3vsizO5/F4XGQYE9gUdYizk4/m0ZHI68deI0vqr4A4JaJt7Bs7LJgfIRRw6RMPL7wcZZkL2F10Wp2Vu/k/aL3AaNvfFcvjpSIFCwmCykRKURYItBoIiwRzE6ZLWvrhqAmZxNfVH7BoYZDNHQ00OHp4FjTMbZXb+/u/bUocxFPX/y0395TEv0I4/Ee7xFxyZhLfJZtc7V1LzRyXf511LTXdA82AqMXyQNzHmBOypyTFv0Q/qGU4qIxF3HRmIsA42+yv34/r+x/hUP1h9hWvY1GR2Of20ZZo8iMyiTBnkCkNRK31027p50mRxMdng6mJk7l7PSzCbeEYzVZcXldODwOOjwdONwOvNpLamQqs5JndU8W1+HuoLCpkKq2KrKissiOyT6p51DXLJ+lLaWUt5RT21FLmDmMVlcrxc3FZEdn4/a6u3daYeYwomzGyc/UiFRSI1PJjMpkbOxY4u3xo2JE5kBcXheflHzCPw/+k09KP+nu2RRuCcdmtpERmcFV466iydnEO0ff8fv7m5X5pBPWoShkEr3VbOWqsVfx7yP/5r3C97hkzCX91ugjrBE8MOcBfrPlNzQ5m3j64qdpdDRypPEIdrOdCfETRkWXq+EkwmrU1HvuWJ0eJy6vi5LmEjzag0mZqGytZF3JOqrbqqlz1FHdXo3VZMVmtpEckYzVZGVd8TreOvLWoN432hqNR3tOGi9gMVlIj0zHq724vW482kOzs7m7Oe+k17FF0+w0Bg11Td7m9DipaqtiV80u6jrqepW3mWzMTpnNdeOv49yMc/ttjqhoreBIwxGONh2lsLEQu8VunHNKnsHYWN9jCoaz+o56Xj3wKq/se4Wq9iqSwpP48tQvsyhzEZMTJvc6aezyuPjqe8ZKTP4+aaqUYjh2SPG3kEn0AN+f/30ONRziO+u+w6LMRTx10VP91prunnY3Lo+Lp7Y9xXVvXEdyeDIOjwO3182yccu4dVIILUQ8QtnMNmxmW68ZRiclTOKC7At8bufyuihrKcPhceDyurCarNjNdsLMYd1NPqUtpWwq30R5azlmZTZGEcfmkhaRRnFzMYcaDlHeWo5FWTCbzFhMFiItkWRGGyN30yLTSApPwulxdnfTrG6rxuV1kRaZdtLvzuFxUNVW1b0QfHFzMZ+WfsoPPvkBYBxFJocnE2mLxOF20OJqoaqtqtcOIsoahdPj7J4PaFriNG6fcju5MbloNF7txau9aDT1HfXdI4mr26qpd9TT6Gikw93R/XoWk4UoaxQJ4Qk8OOdB8uPzz+wPNoA2Vxufln7KB8c+4INjH+DwODg341x+cvZPWJS1qN+T8hWtFd1NqS/seYFbJt7it9lbFWrA3k6hIOS6Vzo9Tp7a9hTP7XqOa8Zdw3fmfaff2pLb6+YfB/7B+0Xv0+5pJ8wcRrOzmX11+1g+fjmPnvvoGXwKIXzzeD0UVBawr24fW6u20uxsptXVit1iJ8oaRWJ4ItnR2cxMnklebB6J9kS82tu9k/jbnr91L5TTn65+83H2OOLD4nud23B5XWyu2ExdRx1Xj7uaX5z3i17bur1uWpwt2My2k/rWa62paa/haONRWlwtzEieQaI9ETg+CMntdXOo4RBbq7byWeln3dNpxIXFcemYS7lt8m2D7pVU2FjIir0rePPwm7S720m0JzI/fT4L0hawMHPhaU80uHjlYi7IuiAk/tdH3TTFWmue2vYU/7vjf7GYLFyYfSG3TrqVs9LOGnBbj9fD3BVz8WgPd065kxsn3siYmMCsCCPEmXB5XByoP0B1ezUmZUKhjGuliLHFkB2dPWCXyzVFa3hw7YOAcVSRaE+kzd1GbXstDY6G7tpuflw+aZFp2Ew2ajtqOdJ4pLupqktXjdztdWMxWdBad7e5Z0Zlsjh7MUtyljA7ZfZpd6ltcbaw5tgaNpZvZFP5Jmraa7AoC7dOvpUH5z54yiOyl6xcwvlZ53cnerfXTbu7HYUacWMORl2i73K44TCvHXiNVUdX0eRo4plLnmFB+oIBtytuKua3X/yWD499SLglnOcuf67fBUqEGMncXjcfFX/E/rr9FDcXU99RT4Q1onvahhhbDM3OZrbXbKehowGn10mMLaZ7ofuxsWOxW+zsqN5BbUctCoXdbMfhcWA2mcmLzWN2ymwyIjP83sdfa82RxiOs2LuC1w68xnmZ5/Hk4id7jSDu+TmPNR3rbpJrdDTS5Gzilf2v9Pv64+PH8+g5jzIjeYZf4w6UUZvouzQ6Grl91e0caz7GbZNu4/5Z9w+qv/LL+17mF5t+wXX51/HzhT/3WzxCCP9auX8lj218jHum3cM90+9hb+1edtfuZm/tXg41HqKwsbDX2sdd4xb669nVJdwSzpob1xBjG/7LCY76RA/GjJTfXfddPin9hG/N/hZfm/E1n+UbHY1c+tql5Mbm8tSSp0gO8LzYQogzM/1v0096LNGeyNSkqYyLG0d+XD75cflkRWcRZY066YS5V3tpc7Vx7/v3srNmJ2D0ynr/xvdPa9BlsI2qKRD6E2mN5JZJt/BJ6SdMSZwyYPndtbtpc7fxwOwHJMkLMUJ1eDq4Nv/aAcfWgFHL/7zi8+4kD3TPSTQSEr0vo2bERmVrJU8UPAHQ59SsJ8qOykah+Ovuv3Yv0C2EGP4yozL57NbPWD5+Oa2uVj449sGgt02PTCc1IrXXSd3y1vJAhBlUo6Lpxu11c9NbN1HaXMqDcx/khgk3DOqs/78O/YtH1j9CTnQOr171qgy7F2IYW1u8lh988gNaXa3GOTht1MhzY3J5ZdkrIT/9sq+mm0HX6JVSZqXUVqXUW533ZymlNiqltimlCpRS8/vZ7nKl1H6l1CGl1A9O7yOcmQZHAwfrD3LrpFu5ZdItg+7adW3+tTx6zqPd65YKIYavC7MvZONtG3l52cucl3le9/KGhU2FfFzy8RBHN7ROpenmAWBvj/u/Bn6mtZ4FPNx5vxellBn4PXAFMAW4VSk1cAO5n3UtLLG5cvOAZVucLeyu2c2m8k3srN7ZPaDj99t+PyqGSgsx0k1NnMqvz/81z1z8DJeMuYQpiVNG/ViYQVVtlVJZwJXAL4Bvdz6sga4+R7FAWR+bzgcOaa2PdL7Oy8A1wJ4ziPmU7ak13m5u6tx+y9S01/DDT37IxvKNfT6/u3Y3Lq9r2C0OLoTo28LMhSN++U9/GWyvm98C3wN6dj7/v8BqpdR/YRwZnNvHdplAcY/7JUCfI5aUUvcC9wLk5PS/KMjp2FK5BYDl45f3W+aprU+xsXwjd0+7m5nJMwkzh7Gvbh87qndwqOEQ+XH5VLdXkxmV6dfYhBAi0AZM9EqpZUCV1nqLUurCHk/dBzyotf6HUuom4Fng4hM37+Ml+2z/0Fr/CfgTGCdjBw598LpWk9pRvaPfQ7jJCcY6sX/Z9Rdiw2JpcjT1muzoWPMxNlduZs0Na0L+pI4QIrQMpka/ELhaKbUUsAMxSqkVwFUY7fYArwJ/7mPbEiC7x/0s+m7iCahzMs5hRtIMHl7/MAWVBdw26baTpjS4edLNpEamsq9uHzXtNSTaE8mLzSM3Npc/7/wzqwtX0+5ulznEhRAjzil1r+ys0T+ktV6mlNoL3Ke1XquUugj4tdZ67gnlLcAB4CKgFNgM3Ka13u3rfQIxMraho4HffvFb3j7yNh2eDiYnTOastLOYlTKLxdmLffbE2VWzi1vfNqYt/vvSvzM9+eQReEIIMZT80r2yD18DnlBKbQd+SWf7ulIqQym1CkBr7Qb+A1iN0WNn5UBJPlDi7HE8eu6jrLlxDQ/Ne4hIayTP73meb6/9NmuK1vjctt3d3n3bZrZR0lzCTz/7KU9+8WSv+b2FEGI4OqUpELTWa4G1nbc/BU7qxqK1LgOW9ri/Clh1JkH6U2xYLHdOvZM7p97Jqwde5ecbfk52dLbPbQoqjaMLq8nKrz7/FdurtnevHH+s+Rj/dcF/BTxuIYQ4XaO6wXl28mwsysItb9/ic0DUnVPu5GvTv8aslFl4tZebJ93MXVPvAowmISGEGM5GxRQIvvRceGF60nSWj1/OORnnkBGV4XO769+8noP1B5mUMIlmZzOPLXxsUAubCCFEIMg0xQNodjbz5uE3Wbl/JUcajxDdpvm/m1OIueQSTCjwakyASYPSoL1eihuLWF/6GUqDyWs8Z0JxXsZCrrv9MaypKUGLXwghJNEPktaaA/UHKP7Zw2Su3nH6LxQRTsKNNxpTJmhAa+yTJhJ3ww1+i1UIIXqS+egHSSnFxISJ5D/2vxz8dAm6tRXr4kVYv3Q9OsyGVgoNKLMJpUwokwlMJto87ZgtViJsUbiefRHHex/R8No/QCnj4vVS39qKslqJveaaof6YQohRRmr0/fC0tFD2/R/Q8sEHmOPjibniCiLOOZuIefOwxMef0mtpt5uiO++ifYsxFcPYt/5NWH5+IMIWQoxS0nRzBlo3bqT+pZdpWbsW7XCAUoTPmkXUksVEL1mCbezYQS167Glu5sBZxkzOCXd+mfhbb8WSnIwpcmSvXCOEGB4k0fuBdjpp37WL1vUbaPnwQzr2GDNiWlJTibn8cpL+4xuYo30vOO6qquLotdfhqavrfsyanU349OnYZ0wnbvnyAV9DCCH6Iok+AFzl5bSs+5iWjz+m5aOPMCcmkvKd7xB7zdVG230/3HV1dOzeg7u2BndFJR179tC+ayfusnKiliwh++nfB/FTCCFChST6AGvftZvKxx6jfft2wmfOJPHr/4eoCy7wmfC7aK1p27CBiscex3n0KHlvvIF94oQgRC2ECCWBmutGdAqfNpUxL/2d9F/+EldFBSX33c+xO+/CXTvwIuQtH33EsbvvwXn0KNGXXkpYXm7gAxZCjCqS6P1EmUzEXX8d+WveJ+3RR2jfvp3Dl19B1W9+S8eBA/0uQxh5zjkkfvUeVEQEze+/T9kPfojj0KEgRy+ECGXSdBMgjoMHqX7ydzSvWQNaEzZhAqk/+TGR8/tcQx13fT11f3mO+hdfxNveTvTll5F0333YJ0gzjhBiYNJGP4RcVVW0fPgRtX/+M66SEmKvvZb0n/8MZet77Vl3fT11z/2V+hUr8La1EX355UbCl3Z7IYQPkuiHAW97OzXP/JHaP/4R+/TppDz0EBHzz+q3D767vp66v/2N+hdW4G1tJfrSS0n6xv3YJ07ss7wQYnSTRD+MNL37LhWPPY6n80Rt/gdrsGb2v+C4p6GB2q6E39JC9CUXk3T//dgnTw5WyEKIEUB63Qwj9mnTsGX3WOhkgC6Y5rg4Uh54gPwP1pB0//20btzE0euup3nt2sAGKoQIGZLog6h95y6OXr8cx8GDpD36KJP27Maanj6obc2xsSR/65vGEUBODjVP/6HfnjxCCNGTJPogKn/4YUyRkeS98S/ib7l5UAOqTmSOiSHxK3fRsWMHbZs3ByBKIUSokUQfRM7CQqLOW4gtK+uMXif2uuswJyRQ++yzfopMCBHKJNEHUfSSJTS8/k/qVryI9nhO+3VMdjvxt3+J1nUf07H/gB8jFEKEIkn0QZT2s58Rec45VD7+OIevWErN//4vHfv7HzXrS8Jtt6HCw6n7y18CEKkQIpRI98og01rT/N771D3/fPdCJObYWGzjxmHLzcUcF4c5JhpltaLdHrSjA3d1Ne7aOhLv/goR8473nqr45S+p//tL5L//3qBP6gohQpP0ox+mXOXltK5fT/u27TiPHsVZVISnqclY4KSLUpiTEvFU1xA2aRK5f38RU0SEsX1pKYcuvYyEO+4g9QffH6JPIYQYDmTN2GHKmp5O3PLlxC1f3utx7XSiXS6wWlFmM8pspv7VV6n46cO0rFtHzBVXGNtnZhKzdCkNK1eSdN/XMcfGDsXHEEIMc9JGPwwpmw1TZCQmmw1lNgPGiVxMJlo++bRXm37iPXfjbWuj/uVXhipcIcQwJ4l+hLAkJpLwlbtofP11iu/5Kq6yMgDskyYRed551L3wAt6eTT5CCNFJEv0IkvLQQ6T+5Ce0FRRQ8bOfdz+e+NV78NTU0PjGG0MYnRBiuJJEP4IopUi4/Usk/p97aVm3jo4DRh/6iAULsE+bRt1fnjuj/vlCiNA06JOxSikzUACUaq2XKaVeAbrmzI0DGrTWs/rYrhBoBjyAu7+zwmLw4m+7jc8+aODD/y4hgw2AwhOxBK9uY88v12FOSgKgqyk/PNrKBbdOxGyR/boQo9Gp9Lp5ANgLxABorW/uekIp9QTQ6GPbxVrrmtOKUJzEEh+PK38ONEEZ2US661DWcIgDV5sJc007oFAm8Lg1hTtqyJmSSP7clKEOXQgxBAaV6JVSWcCVwC+Ab5/wnAJuApb4PTrRr+t/dRkv/HgDcakRXPtg/1+916t54cfr2f1JqSR6IUapwR7L/xb4HuDt47lFQKXW+mA/22rgPaXUFqXUvaceouiL2WxixuIsSvfXU13c3G85k0kxdVEGJfvqqa9oDWKEQojhYsBEr5RaBlRprbf0U+RW4CUfL7FQaz0HuAL4hlLq/H7e516lVIFSqqC6unqgsAQwdVEGANs/KPZZbuLZxvQIh7+oCnhMQojhZzA1+oXA1Z0nVV8GliilVgAopSzA9UC/o3W01mWd11XAP4H5/ZT7k9Z6ntZ6XnJy8il9iNGqsbodgI5Wl89y0Ql2wiItVB9rCUZYQohhZsBEr7X+odY6S2udC9wCfKi1vr3z6YuBfVrrkr62VUpFKqWiu24DlwK7/BK5oL3ZSPBTF/W/5myXmMRwjmyrxuPpq/VNCBHKzrS/3S2c0GyjlMpQSq3qvJsKfKqU2g58DryttX73DN9TdEobG0NkrI11L+6jqabdZ1mljOvWBhk9K8Roc0qJXmu9Vmu9rMf9u7TWz5xQpkxrvbTz9hGt9czOy1St9S/8E7YACIuwctW3ZuF2eXnjf7bhaHf3W7a62Gi26ToKEEKMHjKCZgTTWnN4azUup4e2RgcdLb2TuMfjpXhPHfs2lDP5nDQAinbKcAYhRhuZpngEKz/UwOa3jjJ2djKLbppAVHxYr+c/eeUguz8u7fXY/k0VzLl8DBarOZihCiGGkNToR7CWeqO9/awr805K8gAT56eSMT6O+LQIknOiiUoIo6mmg3f+sDPYoQohhpDU6Eew1LxYlIKCVUe57KvTUCbV6/n0/Diu+86cXo8VrDrKpjePUn2smeSc6GCGK4QYIlKjH8Fik8M5+7pxHP6imk3/PjKobSadYwyyOrJNBqUJMVpIjX6Em31JDg2VbWx5p4i41Agmne17kfDIOBsoKFhViPZq4tMjSc2NIS41IkgRCyGCTRL9CKeU4oJbJ9JU08FHL+xDKcXEBWk+yy/9+nTWv36Yre8dw+s15jKetzSXBVePDVbYQoggUj3XHx0u5s2bpwsKCoY6jBGlo9XFqj/soPxQIyljohl/Vir5c1P7PEnbxeP20lDVxrb3jrFvYwXnXDeOOZeNCWLUQgh/UUpt6W+9D0n0IcTr8bLr4zL2bSin+lgzKMgcH8f0C7MYN6f/KYq1V/P+c3s4uLmSK74+nbGzZK4hIUYaX4lemm5CiKlz6uIZi7NoqGzjwOZKNr91lNrSVp+JXpkU4+elcHBzJWtf3EdjdTtFu2qJSwknJTeG/Lkp2OzyUxFipJJeNyEqLjWC+cvyWHDNWDpaXVQebfJZvnhPHWBMkbD+H4doa3RwsKCKj17Yx4qHN7J3fXkwwhZCBIBU00Lc1EUZ7PmkjDef3Mb8ZXlMPT+jz1Gx5900nknnpuPq8BCdaCc60Q4aKo40sv71w3z4/F5sdrPPIwMhxPAkbfSjQFNNOx++sI/S/fVExYcxdnYyWZMSyBwfhy184H29x+Xln//9BXXlrdzyk/nEJIUHIWohxKmQk7ECgOJ9dWx77xilBxvwuLygIDYpnAu+NJHsSQk+t22qaeflxz8nOTuaax+cfdIoXCHE0JJEL3pxuzxUHGmi/FADez4tw2RW3Pbo2Zgtvk/Z7F1fxofP7+Pc5fnMviQnSNEKIQbDV6KXk7GjkMVqJmtiPGddmceFt0+iqaaDXSfMctmXSeekkzcziQ3/PMyx3bVBiFQI4Q+S6Ee5nCkJZE6MZ/PbR2mp7/BZVinFxV+ZQkJ6JO89u3vAtWqFEMODJPpRzphCYQJet+adP+4y2u59sNktXPyVyTja3XyxuihIUQohzoQkekF8WiQX3TWZqsImPn7lwIDlk7KiyZuRxJ5Py3D6WL5QCDE8SKIXAIybncKcy8ew59MyNv7rMAOdpJ+3NBdHm5vtHxYHKUIhxOmSRC+6Lbh6LGNnJ7Pl3SKqCpt9lk0ZE0PezCS2f1CM2+kJUoRCiNMhiV50M5kU4+elAtDW5Biw/Mwl2Tja3Oz+pCzQoQkhzoAketFL9pQEohPsfLLy4IC9ajImxGGPsvLpqwdpqGwLUoRCiFMliV70EhZu4dKvTqW10cG/n9yGy9F/s4xSiou+PBmAskMNQYpQCHGqJNGLk6SNjeXye6dTdayZtX/f5/PE7JjpicQk2dnzqTTfCDFcSaIXfcqbkcT8ZXkc2FTJsd11/ZZTSjHlvAwqjzbR2jBwu74QIvgk0Yt+zblsDBabiaIBpjvInZ4EQOHOmmCEJYQ4RZLoRb/MFhNxqRGUHWzoXkS8LwkZkUQn2inaJfPfCDEcSaIXPs26OIfakhafk5gppUjJiaa+QnreCDEcSaIXPqXmxgAMONVBTHI4TbXtPmv+QoihMehEr5QyK6W2KqXe6rz/ilJqW+elUCm1rZ/tLldK7VdKHVJK/cBPcYsgMZmNBUY8bt+TnUXE2PC6tc/umEKIoXEqa8Y+AOwFYgC01jd3PaGUegJoPHEDpZQZ+D1wCVACbFZKvam13nMmQYvgsUdZAWPRcF+6Fi1xdbgJG8TyhEKI4BlUjV4plQVcCfy5j+cUcBPwUh+bzgcOaa2PaK2dwMvANacfrgg2m92CJcxM+eGT9uO9tDU7AWiply6WQgw3g226+S3wPaCv4/dFQKXW+mAfz2UCPac3LOl87CRKqXuVUgVKqYLq6upBhiUCzeP24nZ4CO+s2fcnc3wcAPUVrUGISghxKgZM9EqpZUCV1npLP0Vupe/aPEBfK0j3ebZOa/0nrfU8rfW85OTkgcISQVK81xgsNXa2779JfFokAG6n77Z8IUTwDaYxdSFwtVJqKWAHYpRSK7TWtyulLMD1wNx+ti0BsnvczwJkrPwIcnhrNdYwM1mT4n2Wq++c1Cw2OTwYYQkhTsGANXqt9Q+11lla61zgFuBDrfXtnU9fDOzTWpf0s/lmYLxSKk8pZevc/k0/xC2C5Oi2asbOSsZiNfssV7ynDqWMeeqFEMPLmfajv4UTmm2UUhlKqVUAWms38B/AaoweOyu11rvP8D1FEJktJlRfDXA9uBwetr5XREpuTHcvHSHE8HFK/eC01muBtT3u39VHmTJgaY/7q4BVpxugGFpJ2VFUF7f4LLPupf1oDZVHm3j799sZNyeF/HkpAx4FCCGCQ0bGCp/SxsZSV9ZCTUn/SwuOn5dKRKyNlNwY6ira+OBve/njN9dRtKsWLSNlhRhyaqBFoIfCvHnzdEFBwVCHIYD2Ficv/fxzTAqu/MZMknOifZbXWvP0fR9137fazVx855QBe+0IIc6MUmqL1npen89JohcDqS1t4Y3/2UZ7k5PknGgmLkgjf14KkbFhfZZvrG6ndH89zg43Bz6vpL68lRt/dBYJ6ZFBjlyI0UMSvThj7c1O9m+q4MDnlVQfa8ZkUiy5czITF6T53K610cHLj31OZGwYN3x/LhabtNsLEQi+Er200YtBCY+2MeviHG760Vnc9ugC0sfH8sFf93Bkm+9RzJGxYVx81xRqS1s6T9oOv4qFEKFOEr04ZfFpkVx5v9Fev+a5PTRU+p6Hfsy0ROYtzWXfhgr2b6oIUpRCiC6S6MVpsYaZueLrM9Bas+WdwgHLn7Usj4zxcXz88gEcbb5nwhRC+JckenHaouLDmHZ+Jvs2VVBZ2OSzrMmkOO/G8bg6POz5tDxIEQohQBK9OENnLcsjLNzCF+8WDVg2OSeazAlx7PioGK9HJj8TIlhkhQhxRmx2C9MuyKTgnSJ++o01hJlNmEwnTFva4/yrzQ12D6z/tITzLsgJdrhCjEpSoxdnbNZFOXgy7bQoTZ32oMPNeMPNeMMtxiWi8xJpoSPWwj6rm49K64c6bCFGDanRizNmj7Lyte/PZ9ojq/mPJeP59iUTfJa/5qlPqW/rCFJ0Qgip0Qu/iLBZGJMYyYGK/ufE6ZIcHUZdqzMIUQkhQBK98COTAtcgTrIW17VjNSs8MuGZEEEhiV74zfy8RDYcqaWi0XezzLTMWLaXNHL+rz+itkUWExci0CTRC7+59/yxaA0PvLwVt4+a/X8un84TN86kvLGdP6w9HMQIhRidJNELv8lLiuTxa6ex6WidzwRuNZtYPjeL5XOyeH5jEeWN7UGMUojRRxK98Kvlc7M4f0Iyf9tQSFWz7yacBy4ej9aaJz84GKTohBidJNELv/veZRNpdXj46t8K6HB5+i2XFR/BlxaMYWVBCUdrWoMYoRCjiyR64XfTMmO5aHIKO0oa+fxonc+y31icT5jFxCNv7pYpjIUIEEn0IiCaO9yMSYxgYX6Sz3LJ0WH88IpJfHygmhWbjgUpOiFGF0n0IiBSosOoaXZQ1jDwidaqZqOL5fbihgBHJcToJIleBMQ3Fufj9Hj52/rCAcv+9bNCFo1P4lfXTw98YEKMQpLoRUDkJkUyMS2ag1UtA5ZNjgkjzGLCapafoxCBIP9ZImAibBbaffS66WJWCodb5qcXIlAk0YuAibFbaWgbePKyBWMT2HSkzudoWiHE6ZNELwJmWmYMB6taBpzPZkFeIk6Pl+0ljUGKTIjRRRK9CJiLJqWiNXxysMZnuVnZcYD0uhEiUCTRi4CZnB6N1azYX+l7jvrshAgSI20cGKCcEOL0DDrRK6XMSqmtSqm3ejz2TaXUfqXUbqXUr/vZrlAptVMptU0pVeCPoMXIYDGbyEmI4Gj1wNMb5CVFcmQQ5YQQp+5UlhJ8ANgLxAAopRYD1wAztNYOpVSKj20Xa619H7+LkJSXFMWRmoG7WI5NjuTDfVVBiEiI0WdQNXqlVBZwJfDnHg/fB/yn1toBoLWW/1JxkrHJkRTWtg24mtTY5ChqWpw0truCFJkQo8dgm25+C3wP6Nn/bQKwSCm1SSm1Til1Vj/bauA9pdQWpdS9px+qGIniIqw43V4cbt/96RMibAADrk4lhDh1AyZ6pdQyoEprveWEpyxAPHA28F1gpVJK9fESC7XWc4ArgG8opc7v533uVUoVKKUKqqurT+lDiOErMdJI4AMtBh5mNX6Kg5kbRwhxagZTo18IXK2UKgReBpYopVYAJcDr2vA5Rm3/pKkKtdZlnddVwD+B+X29idb6T1rreVrrecnJyaf1YcTwkxJtB45PXNafyekxALQ63QGPSYjRZsBEr7X+odY6S2udC9wCfKi1vh34F7AEQCk1AbABvU64KqUilVLRXbeBS4Fd/vwAYnhLjg4DoKrJd5OMxWQcDLo9Mie9EP52Jv3o/wKMVUrtwqjp36m11kqpDKXUqs4yqcCnSqntwOfA21rrd88sZDGSpMYMrkbfNaGZS6ZBEMLvTqV7JVrrtcDazttO4PY+ypQBSztvHwFmnmmQYuSKi7AC0NDmuzdN19kdr6wyJYTfychYEVBds1dG2Mw+y1V2Nu2kdB4BCCH8RxK9CKg2h5HoI8N8HzyWNhiJPisuPOAxCTHaSKIXAdXiMHrRDFSj7+is+dutvssJIU6dJHoRUNWdJ2ETI8N8liupN/rPx9itAY9JiNFGEr0IqINVxoyUY5MjfZY7VNXMmMQIYiMk0Qvhb5LoRUBtPdZAbLiV9FjfJ1l3lzUxOS0mSFEJMbpIohcBs+FwLf/aVspVM9Ppe3YMQ2VTB0W1bcwZExe84IQYRSTRi4DQWvOLVXvIig/nx0un+Cz7zs5yAM7Ll6kvhAgESfQiIFZsLGJXaRP3X5hPuI8eN60ONysLSshPiWJKhjTdCBEIpzQyVojBOFDZzM/+vYdF45O45azsfsvVtTq58slPKG/s4OkvzQlihEKMLlKjF37V1OHinr9tJtxm5okbZ/psm99SVE95YwfXzc5k6fT0IEYpxOgiNXrhV//8opTiunZeuffsAaczeGdXOTaziZ9dMzVI0QkxOkmiF37V3GFMXvbd13ZgUuDRGq8XPF7defv4dVOHMWp23f5qrpqZMZRhCxHSJNELv7rz3Fyqmh3UtToxmxQmZVzMJrrvd103dbgob+hgembsUIctREiTRC/8Ktpu5efXTBvqMIQQPcjJWCGECHGS6IUQIsRJohdCiBAniV4IIUKcJHohhAhxkuiFECLESaIXQogQJ4leCCFCnNJaD3UMJ1FKVQNFQx3HEEoCaoY6iGFKvpv+yXfTv9Hw3YzRWve5qMOwTPSjnVKqQGs9b6jjGI7ku+mffDf9G+3fjTTdCCFEiJNEL4QQIU4S/fD0p6EOYBiT76Z/8t30b1R/N9JGL4QQIU5q9EIIEeIk0QshRIiTRB9ESqkblVK7lVJepdS8Ho9fopTaopTa2Xm9pI9t31RK7erndXOVUu1KqW2dl2cC+TkCIVDfTefzP1RKHVJK7VdKXRaozxAop/PdKKXeVUpt79zuGaWUuY/XHZW/m8F8N53lRvTvphettVyCdAEmAxOBtcC8Ho/PBjI6b08DSk/Y7nrg78Cufl43t7/nRsolgN/NFGA7EAbkAYcB81B/3kB/N0BM57UC/gHcIr+bU/puRvzvpudFavRBpLXeq7Xe38fjW7XWZZ13dwN2pVQYgFIqCvg28HjwIg2+AH431wAva60dWuujwCFgvn+jD6zT+W601k2dj1sAGxCSvS4C+N2M+N9NT5Loh5/lwFattaPz/mPAE0DbANvlKaW2KqXWKaUWBTTCoXM6300mUNzjfknnY6HmxO8GpdRqoApoBl7rZ7vR+LsZzHcTUr8bWRzcz5RSa4C0Pp76sdb6jQG2nQr8f8ClnfdnAfla6weVUrk+Ni0HcrTWtUqpucC/lFJTe9RchoUh+m5UH48Nu9qtP7+bLlrry5RSduBFYAnw/gmbjrrfTZdBfDcj4nczWJLo/UxrffHpbKeUygL+CXxZa3248+FzgLlKqUKMv1WKUmqt1vrCE97TATg6b29RSh0GJgAFp/UhAmQovhuMmlh2j/tZQBnDjJ+/m56v26GUehOjKeL9E54bjb+bnq/b73fDCPndDJY03QwDSqk44G3gh1rrz7oe11r/QWudobXOBc4DDvSRyFBKJXf1HFBKjQXGA0eCEHrAnel3A7wJ3KKUClNK5WF8N58HPPAg6O+7UUpFKaXSO29bgKXAvj62H3W/m8F+N4Ta72aozwaPpgtwHUZNwQFUAqs7H/8J0Aps63FJOWHbXHr0kACuBn7eeXs5xgmn7cAXwFVD/VmHy3fTef/HGL0m9gNXDPVnDfR3A6QCm4Ednb+L3wEW+d0M/rsJhd9Nz4tMgSCEECFOmm6EECLESaIXQogQJ4leCCFCnCR6IYQIcZLohRAixEmiF0KIECeJXgghQtz/A272PbVZiXnCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oceanTracts = [1094,1690,1239,607,785]\n",
    "for t in oceanTracts:\n",
    "    x,y = tractGeom[t].exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    isSkippedTract[t] = 1\n",
    "                \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1542 13\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "#isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baabb633-83ac-4315-92bd-1d6fdd9e999b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, manually flag tracts that should be excluded from the map, such as lakeshore empty tracts\n",
    "isSkippedTract = [0]*nTracts\n",
    "#skipList = [1108,2676]   #Lake Erie and northeasternmost Susquehanna.  All others are small.\n",
    "for s in skipList:\n",
    "    isSkippedTract[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>CNTY_FIPS</th>\n",
       "      <th>CNTY_NAME</th>\n",
       "      <th>COUSUBFP</th>\n",
       "      <th>MCD_FIPS</th>\n",
       "      <th>MCD_NAME</th>\n",
       "      <th>CTV</th>\n",
       "      <th>LABEL</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>ASM</th>\n",
       "      <th>...</th>\n",
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       "      <th>G20PREOHAW</th>\n",
       "      <th>G20PREOWES</th>\n",
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       "      <th>G20PREOCHA</th>\n",
       "      <th>G20PREOSIM</th>\n",
       "      <th>G20PREOWEL</th>\n",
       "      <th>G20PREOBOD</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>55001002750001</td>\n",
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       "      <td>C</td>\n",
       "      <td>Adams - C 0001</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
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       "      <td>5500100275</td>\n",
       "      <td>Adams</td>\n",
       "      <td>C</td>\n",
       "      <td>Adams - C 0002</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
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       "      <td>5500100275</td>\n",
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       "      <td>C</td>\n",
       "      <td>Adams - C 0003</td>\n",
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       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-89.79773 43.96678, -89.79772 43.965...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55001002750004</td>\n",
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       "      <td>Adams</td>\n",
       "      <td>00275</td>\n",
       "      <td>5500100275</td>\n",
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       "      <td>C</td>\n",
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       "      <td>41</td>\n",
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       "      <td>POLYGON ((-89.81767 43.95958, -89.82400 43.959...</td>\n",
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       "    <tr>\n",
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       "      <td>Adams</td>\n",
       "      <td>00300</td>\n",
       "      <td>5500100300</td>\n",
       "      <td>Adams</td>\n",
       "      <td>T</td>\n",
       "      <td>Adams - T 0001</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-89.71671 43.89444, -89.71701 43.894...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            GEOID CNTY_FIPS CNTY_NAME COUSUBFP    MCD_FIPS MCD_NAME CTV  \\\n",
       "0  55001002750001     55001     Adams    00275  5500100275    Adams   C   \n",
       "1  55001002750002     55001     Adams    00275  5500100275    Adams   C   \n",
       "2  55001002750003     55001     Adams    00275  5500100275    Adams   C   \n",
       "3  55001002750004     55001     Adams    00275  5500100275    Adams   C   \n",
       "4  55001003000001     55001     Adams    00300  5500100300    Adams   T   \n",
       "\n",
       "            LABEL DISTRICT ASM  ... G20PREICAR G20PREOHAW  G20PREOWES  \\\n",
       "0  Adams - C 0001       41  41  ...          0          0           0   \n",
       "1  Adams - C 0002       41  41  ...          0          0           0   \n",
       "2  Adams - C 0003       41  41  ...          0          0           0   \n",
       "3  Adams - C 0004       41  41  ...          0          0           0   \n",
       "4  Adams - T 0001       41  41  ...          0          0           0   \n",
       "\n",
       "   G20PREOLAR  G20PREOCHA  G20PREOSIM  G20PREOWEL  G20PREOBOD  G20PREOWRI  \\\n",
       "0           0           0           0           0           0           1   \n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "3           0           0           0           0           0           0   \n",
       "4           0           0           0           0           0           0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-89.82780 43.96684, -89.82780 43.966...  \n",
       "1  POLYGON ((-89.80944 43.95238, -89.81334 43.952...  \n",
       "2  POLYGON ((-89.79773 43.96678, -89.79772 43.965...  \n",
       "3  POLYGON ((-89.81767 43.95958, -89.82400 43.959...  \n",
       "4  POLYGON ((-89.71671 43.89444, -89.71701 43.894...  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/wi_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7090 0.4968093673346601 3241050 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic WA map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-87.8,44.7)\n",
    "pt2 = Point(-87.2,44.3)\n",
    "pt3 = Point(-86,46)\n",
    "pt4 = Point(-87.1,46)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "#wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6a63312-1449-4619-a6db-7bff3da48f10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l1M+zV2sSoreVVdfzv1/uIMTXA5OCC4dHnzT7pay6nk0Hygjz82TVnmIWbzlEeU0DQ6MDOXKslk0HygAYFR/E4Ch/tuaXU99o5f5ZA9lbVEVUgBcXjYiWRb96wJkE+F+AudjeqPTCNga+AAgD/qq1Xm4/by8wXmtd1NZj9VaAHyit5sFPt6AUbMg72rw29G1Tk3n4wlTAdtWcu9kkV78J0QFaa95Zk8sfFm3H18OM2aSwWDVV9ZaTzh2bGMLNU5JIjQpoHoYRZ+aMhlBaPMh0jvfAbwditNZ/UEoNBJYCCUb2wPNKqnng082s21cK2NajuOqsBCakhJKeGCxXtQnRzQqO1XLwaDV+nu4UHKvliS+3k1NU1Xz/U5cOZe6EROMK7CO6YxbKid4E3rQPpdQDN7QX3j3pWG0Df1y0nc835aM1zBkTx23Tkk9ahEgI0b0iA7yIDLB1jAZF+bPs/umU1zRw1/sbWJldzNdbj3Dp6FgCvNwNrrRv6lQP/Ez1VA/8lvkZLNtVwK1Tk/n1xCSiAqWnLYSRCitqGfv0UgCmDAjjnZvHGVyRc+uJHrjh6hotvLgkmyU7C/jtjP7cP2uQ0SUJ4fIaLVa+aLFQ16WjYg2spm9z6gD/fnsBry3fS2p0APOmJhtdjhAC+M37G/huewGT+ofy4q9GE+4vs7x6ilMHeNOblbdMTsJfxtiEcAh+nrbfxZ2HK1i7r4QQHw/W7ivl4NEaCitqGREXyO/PG2xwlX2D0wb4nsJK3luby/lDo5g9MtrocoQQds9ePoJRCUE8++0u7np/Y/NxL3cTtQ1WVmYX4+VmZtbQKKrqGwn19SAhxEeufO4Cp3sTs7q+kQUb8vnrN7swKfj2nqnEBHl3U4VCiO5SWdfI/uIqjtU24O/pzvC4QBZtyufuDzed8vyzB4Xzz7npeLjJUrkn6pZ54GfqTAL8UFkNj3++jeVZRVismrGJITx/xUi5UEAIJ1Nd38jPe0uobbCi0WQVVPLBujyKKuqYmRrBvKkpjIoPkiBvwelnoby4JIuluwq5ZlwCF4+IYXxyiLzkEsIJ+Xi4cU5qZKtj95wzgCcX7+CDdXks2VkIwMbHzyXYV9Ykao/T/In7fNMhpg8K58+XDWdCSqiEtxB9iMmkeOKSoax/bCZp/WxrtJz7wo+U1zQYXJljc4oAb1pdbXJ/WatYiL5s2c5CMnOPAuDtYcbdLB219jhFgK/Isq2PdcFwmW0iRF8W6mcbMvF0M/HZ7RPx8XCaUV5DOEWAN1hsqwlaLIYstSKE6CVTBoTz0lWjaLRqpj23nAOl1UaX5NCcIsADvW0XBqzZV0JlXSNgW96yorYBg9bPEkL0gAaLlXOHRHLTpERqGizNa5CLU3OK1yf9I2yrCj746RYe/HQLgd7uuJsVxZX1DI7y54aJiVw2OlYWkhfCyf36rfWs2lMMgK+HmakDww2uyLE5RQ88rV8wPzwwnVsmJwEwLimEaQMjuHfmQJRSPLxgKxe9vFJ640I4uSExxzcSn3/T2OZX3+LUnKIHDpAU5stjs4fw2OwhrY7/7pz+3PneBr7ZdoSiyjrZtEEIJ/bIhakoBf/8MUe2OuwAp+iBt0cpxR3TU/DxMHPV62soOFZrdElCiDNwwTDbbLPPN+YbXInjc/oABxgRF8Tbvx5LQXktF7y0kn/+uJeKWrkAQAhnsy2/nL98vROAWUMjT3O2cJohlNMZmxTCR7dN4NGFW/nLN7vIzD3K69eftHSAEMIBWa2ap7/eyRur9uHn6cazc0YwNCbQ6LJ45+f9fJxxEACTApRC2W8rpWyfUSgFSoFJ2W83H7Od4+vpxpOXDO32YaE+E+AAw2IDWXTXZG6Zv57dBRVGlyOE6KAt+eW8sWofk/uH8eo1Ywj0Mf7Ny88yD/L4ou0MjQkgwt8TqwaNbQqz1qDRWK32zxq0FSxYbbe1bj6/sraBvUVVXJkez7RunlXTpwIcYOvBcnYdqZCVzIRwIr4etinAZpM6ZXiXVdezeMth4oK9mTYwvMfXQlqRVcRDn21hUv9Q3rpx7BnlyYqsIq5/cx09UXGfCXCtNV9tPcxd728k0NudZ+aMMLokIUQH1DZYOPeFFQBMH9S6h2q1ahZszOcPi7ZRXW8B4IJhUbzwq1E9dt3Htvxy7ng3kwGR/vzjurQzCu/Ve4u5+8ON+Hu5MTDSvxurtOkzAb56bwl3vb+RUF8PFt45SdYJF8JJvLQ0u/n2y0uzeWPVPuoarQR5u3PwaA01DRbGJoXw6IWpLNhwkPk/55IancPvzhnQ7bWsyCrivo83EeTjwdu/PqvLWzVqrZm/ej9PfbWTpDBf/nV9OlGB3T/FuU8EuNaa5bttawj/+ZfDJbyFcCKXjIxhU14ZP+eUcLS6gWGxgcQEelNUWUdUoBczBkdw3fh+uJtN+Hu5Mf/nXHYcOtatNVTUNvD0Vzv5cP0BUsJ9+efcdCIDuha4dY0WHv98Gx9nHGRmagQv/GpUj+3Z6/QBnpl7lJeXZvNjVhEzUyNPegkmhHBsqdEBfDBvPLUNFrS2LSN7Kuv2lXLV6z8T5OPOoxeldtvzr8gq4n8+28KRY7XcNi2Ze2cO7PLwTHlNAze9vZ7M3KP8bkZ/7pk5EJOp58brnTrAc4oqmfPaagAenz2EGycmYu7BfywhRM9pLzRLq+q5Zf564kN8eO3aNOJDzvxVdkVtA3/+eicfrDtAcrgvn94xkTEJwV1+vNKqeua+sZbsgkpevWYMF43o+eWvnTrAg3xsawdfMCyKm+3rpAgh+p5nv93FsdpG3vr1Wa3WS+mqVr3uqcnce27Xe90AxZV1XPuvtewvqeL169OYPijijGvsCKcO8KZ1wkfEBRlbiBCix/y0p5gP1x9g2sBw0vqFdPlxSirr+GLzIRZsyGdrfnm39LrBNsXxun+vJa+0mrduPIuJvbhzmFMHeNNgye4j3fuGhhDCMTRYrDz55Q4Anrx0aJceY1t+OS8uyWb57kIarZqhMQH8YfYQrhmXcMZTEStqG7jhzXXkFFfx5g29G97g5AH+2QbbYjfnDY0yuBIhRE9YkVXE7oIKpgwIo1+ob6e/X2vNPR9toqSyjpsmJ/HLMbEMjjrzIRiA6vpGbn47g+2HjvGP69KYPKD39+x16ssVxyWHYFLw9bYjRpcihOgB6YkheLiZyMw9ykfr87BYO7fm/64jFewprOT+WYN45MLUbgvv2gYLt72TSUZuKS9eNYqZQ4xZeMupA3xMQjATUkLZeViGUIToiwK93flw3nhSowN46LOtzP77qk7tk/nl5kOYTYoLhnXfq/QGi5W73t/Iyuxinr18JLNHxHTbY3eWUwc4QFyQD3sKK8mWxauE6JPGJATz6e0TeHz2EHYePsZFL6/k08yDbMsvb7dHrrXmi82HmJgS2q2rAP592R6W7CzgqUuHcnlaXLc9blc47Rh4ZV0jLy/N5qOMAwR6u+Pj6bRNEUKchlKKmycnEebnwYOfbuGBTzY33+dhNpEU5ktcsDexwd7EBnnTL9SXYB/bpfj3zhzYrbXsOFTO4Ch/5k5I7NbH7QqnTL3Sqnou/vsq8stquDI9jgfOGyRbqQnhAi4dFcusIVEcPFrdvABWvcXK7oKKk5aQjg70wtPN1O0bQxRV1hPu7xjbvXV4CEUpZVZKbVRKLbZ//YRSKl8ptcn+cWHPldlaXmk1+WU1xAR6MW9qioS3EC7E28PMwaM1AKT3C2bKgDCiT1goys2kKKmqZ+aQyG5fh6S4oo5wB9mvszM98LuBnUDLt3Ff0Fo/370lnd7IuEAeuyiVP321k4v/vooVD57tMH8RhRA9b4d94sI7N4/D28NMaVU9AAePVrOnsJLXV+Swu6CCX09M7NbnfXdNLvllNQzogaVhu6JDPXClVBxwEfDvni2nY5RS3DIlmTljbG8g+Hr2zLrAQgjH5GG2RZdFa9bmlDD5mWXMeW01dY1Wgn082HWkglunJJOe2PUrN0/0xeZDPL5oG+cMjuCWKY6xdEdHe+AvAg8CJ/7ZuUspdT2QAdyvtT564jcqpeYB8wASEhK6Xukp1DZa8PYw4+kmAS5EX6O1ZufhCg4creZwWQ2Hy2sprarHqiG70DbevSq7iHs/2kx0oBf1FitX/vNnfD3cGBzlz/2zuu/Nyx92FXLfR5s4KzGEV68dg7vZMSbwnTbAlVKzgUKtdaZSanqLu14DnsK27dtTwN+Am078fq3168DrAOnp6Z2bhX8a45NC+GrLYf61Mofbp6V050MLIQz2xeZD3P3hpuavPdxMhPh4YDYpTPb8/O0HG+kX6sv7t47D18ONv36zi2+2HeaFX43qcseu0WIl1z7XXAH7S6q4870NDI72540b0ntsJ6Cu6EgPfBJwif1NSi8gQCn1rtb6uqYTlFL/Ahb3UI1tmjshkbdW72f13hIJcCH6mGM1DQC8e/M4Bkf7E+rr0bwX5uo9xdw0fz3xwT68f+v45vfAnvrFMJ68dGiX98wsq67nyn/+TFZBZavjyeG+zP/12B7bmKGrThvgWuuHgYcB7D3wB7TW1ymlorXWh+2nXQZs66ki2/L+2jxyS6q79SorIYRjGRztT1iLWR8/7Snm5vnrSQixhXfYCTNCuhreH2cc4MFPtwAQ7u/JYxelojUoBVMGhBPi69H1RvSQM5kH/qxSahS2IZT9wG3dUVBnvL16Hxar5ubJyb391EIIA6zKtoV3Upgv790yrluvsFyVXQzAs5eP4Mr0+G573J7UqQDXWi8Hlttvz+2BejolMdSX7MJKzF38iyuEcHza/s7Ziqwibv1PRreGd029hWW7Cvlu+xG+2HwIgEEOMkWwI5zySswmXu5m3E0mAryduhlCiFNp0TH70R7eKeF+vHfLuG4ZztBaM/eNtWTkHiXU14Nfpcdz8cgYRsYHnfFj9xanTb7y6gaW7ixgbFJIl8e8hBCO78esIh5ZuJX+9vAO7obwtlo1//ffLDJyj/LIhYO5eXKyU+6n65QBrrXmue93UVVv4YHzBhldjhCiBz346WZSowN475ZxzfvgngmLVXPbOxks2VnInDFx3DQpySnDG5w0wL/bfoR31+Qxd3w/RsYFGl2OEKIHDY0J5J2bx3ZLeAN8vfUwS3YWcsOEfjxxSdenHDoCx7icqJN2H7HN0ayut7Amp7TTu3QIIRzfpJRQrh2XwLs3d0/PG6CqrpE/frGdgZF+PHxhqlOHNzhpD3ze1GRKq+r4OOMgn204SGSAJ787ZwBXn5WAyUlfCgkhWksO9+Ppy4Z362N+tuEgpVX1/Ov6NIe6orKrnLIH7u1h5n8vHUbm4zP5+9Wj6Rfqy6MLt3H5P1azS3aoF0Kcwt6iSp7/bjdjEoIYkxBsdDndwikDvImPhxsXj4zho3nj+dsVI9lXXMXsl1fx1OIdndo3TwjRt9U1WrhlfgbuZhMvXTXa6YdOmjh1gDdRSjEnLY6l90/nstGxvPXTPqY+9wMPL9iCVcbHhXBpWmue/243+4qreP6KkcSH+BhdUrfpEwHeJMTXg+euGMmqh2ZwwbAoPlh3gA/XH5AQF8JF1dRbeHjBVv61ch/XjU/g7MERRpfUrfpUgDeJCfLm5snJKAWPLNxK8iNfc8v8DKrrG40uTQjRS8prGrjgpRV8uP4At01N5slLhhldUrdzylkoHZHWL5iVD57N0p2FrMkp4ZttRxj1v//lqrHxpIT7EeDtxqT+YYT7edJo1c3rqcgsFiH6hpeXZrO/pJp/X5/OzCHdu7Gxo+izAQ4QF+zDDRMTuWFiIrfMz2DJzgI+yzxIVb2l+ZwgH3fKqhuav3Y3K+KDfbg8PY7bpqY47RVaQriy7IIK3vppH9eOS+iz4Q19PMBb+tf1adQ1WvF0M1FYUUdRRR0/ZhWx+0gFyeG+KBQaTW2Dlc0Hynj2290oFHdMl40ihHA2Ly/bg7e7mftn9e2lNlwmwJVSzRP3IwO8iAzwYljsqS/D11pz3RtreXlpNrdNTZZhFSGcyIHSahZvOcRtU1McchOG7tQn38Q8U0oprkiLp6bBwu8/3UJ5iyEWIYRjW55VhNa4xE5dLtMD76xLR8WQU1zFK8uy+WrrIW6ZnMydZ6fg4yH/ZEI4KqtV89aqfQyPDWSECyx0J2nUBqUU9507kAuHR/Ha8r288sMe3li1jwGRfvSP8GNAhD8DIvwYlRB00p58Qghj7DxyjJziKp69fESfudqyPRLgpzE4KoCXrhrN9RP6sXjLYfYUVvLTnmIWbMgHwGxSjEkIYlL/MAZE+HPe0EjczDIyJYQRvtpi22d9XFKIwZX0DgnwDkrrF0Jav+M/FOU1DWQXVPBjVhE/7C7kxSXZAEQFeDEnLZYr0uJJDPM1qlwhXE5do4W3ftrPJSNj6BfqGr97EuBdFOjtTnpiCOmJIdw/axA19RZW7Snmg3V5vLZ8L6/+sJfEUB9umZLMteMSXOLlnBBGWpNTSk2DhWkDw40upddIgHcTbw8z5w6J5NwhkRwpr+XzTfks2VHAY59v4787Cnjo/MEMiQkwukwh+iSrVfPIgq1EB3pxvgvMPmkig7U9ICrQi9unpfDxbRN49MJUNh0o44a31lFRK9MRhegJP2YXkV9Wwz0zB+Dr6Tr9UgnwHmQyKW6dmsy0geEUVdSx/ZBsNiFET6ixL4/hKmPfTSTAe0FiqG394fs/3iy9cCF6wOQBYYT5efDnr3e61B65EuC94L5Zg3ju8hHkl9XwccZBo8sRos8J8HLnjxcPZcvBct5evd/ocnqNBHgvmZlqWxFtbU6JwZUI0TfNHhHNtIHhvPjfLBotVqPL6RUS4L0kyMedy0bH8v2OAt7+aR+1DZbTf5MQosOUUqT1C6airpFGFxlGkQDvJUopHrkwlWGxATzx5Q4m/nUZ328/YnRZQvQZ5dUNfLH5ECnhvs0rj/Z1rjPfxgGE+3vy5V2TWb23hL9+s4t572QyLDaAX4yK5Yr0eAK93Y0uUQinY7VqlmcV8vRXOzlQWsO/b0g3uqReo7TuvZca6enpOiMjo9eez5HVNlh4b20eX2w+xOYDZZhNioQQH84ZHMGD5w/Gw01eHAnRnr1FlcxfvZ/FWw5TWlVPYqgPf/rFcCYPCDO6tG6nlMrUWp/0l0kC3AFsyy/n221H2Jpfzo9ZRYxPDuGJS4YyOEqu3BTiRPWNVh76bAsLN+bjYTZx/rAozkmN4MLh0bj30YXk2gpwGUJxAMNiAxkWG4jVqrnhrXX8tKeYC19ayWMXDeGmyUlGlyeEQ/l8Yz4LN+Zz06Qk7jw7xaWXc+7wnyullFkptVEptfiE4w8opbRSqu+9bullJpPinZvHseHxc5kxOIInF+/gF6/+xBebD9HgItOihDid1XuL8fdy47GLUl06vKFzs1DuBna2PKCUigfOBfK6syhXF+TjwWvXpfHArIGUVtXzuw82cv0b61iw4SAb845SeKxWAl24pKKKOn7OKSE+2Ef2qqWDQyhKqTjgIuBp4L4Wd70APAgs6v7SXJu72cRdMwZwx/T+vL82l6e/3snPLS4CUgrO6hfC8LhABkX5M21gOJEBXgZWLETParRY+e0HGyivaeCtG8caXY5D6OgY+IvYgtq/6YBS6hIgX2u9Wda67jlmk2LuhESuGptAbkkVuSXVHCqr4VB5LUt2FPDOmlzqG614u5uZf9NYxrrITiTC9by/Lo81OaX87YqRsjSz3WkDXCk1GyjUWmcqpabbj/kAjwKzOvD984B5AAkJCWdSq0tzN5voH+FP/4jmv6E8dP5grFbNriMVzHsng+vfXMtffjmcX4yKlQ0kRJ/z2YZ8RsQFMictzuhSHEZHxsAnAZcopfYDHwIzgHeAJGCz/XgcsEEpddJK6lrr17XW6Vrr9PBw19kpo7eYTIohMQEsuHMiAyP9ufejzVz9rzUUHqs1ujQhus381fvZfKCMcwZHGl2KQzltgGutH9Zax2mtE4GrgGVa6zla6witdaL9+EFgjNZarg03SIS/FwvvnMTTlw1jQ14Zv/1gY/MayUI4M601Ly3NJq1fMHdMTzG6HIfSN2e9uyizSXHtuH48PnsI6/aXcvk/VrP9ULnRZQlxRnYcPkZpVT1XpsfJFcon6NS/htZ6udZ69imOJ2qti7uvLHEm5o7vxytXj6HgWB03vLme8hrZREI4r++3F2BSx5dkFsfJn7M+6qIR0bxyzWiOVtdzwYsrpCcunFZFbSMebib8vWSxtxNJgPdh45ND+eT2CVi05qHPthhdjhBdkhzuS22DlaLKOqNLcTgS4H3cmIRg5k1NYVv+MX7eK7sBCefTP8IPgMzcowZX4ngkwF3ANWMTiPD35LcfbGRfcZXR5QjRKWclhpAQ4sMz3+yiRHrhrUiAuwBvDzMPnT+Y4so6NuZJL0Y4F7NJ8co1o8kvq+GfK3KMLsehSIC7iJ2Hj+FmUswaetK1VkI4vNKqegCiZL2fViTAXUB5TQPvr8tjXHIIfp6yBLxwPtX2i9JGxgcZW4iDkQB3AVkFFVTXW7hxomwOIZzTiLhAAHYdOWZwJY5FAtwF7DpSAUBKuK/BlQjRNR9nHARsS0aI4yTAXUBSqC24v90uS9UI5/PvlTm8vDSby9PiOGdwhNHlOBQJcBcQG+wNwH6ZQiiczPtr8/jTVzu5aHg0z8wZIbvwnEAC3AWE+HjQP8KPTzIP8uaqfUaXI0SHrN9fyqOfb2XG4Ahe+NUozBLeJ5EAdwGBPu58/ptJpEYF8KevdlBd32h0SUK0q7bBwkOfbiE2yJu/Xz1aViFsg8wpcxF+nm5U1Tfi7W7G081sdDlCtOuhz7aQU1zFv69Px1emvrZJ/qy5kMRQXxqsmhXZRUaXIkS7ftpTgptJMWVgmNGlODQJcBfyzJwRBPu48+KSbKNLEaJdjVYrV49NkFeLpyEB7kIi/D2JCfKmslY2eBCOa8ehYxyracDfS4ZOTkcC3IUs2pzPxrwyxiQEG12KEKdU22Dhvo83Eernya1Tko0ux+FJgLuQ1OgAfDzMrN5bQn5ZjdHlCNFKZV0jt/4ng90FFTwzZzjBvh5Gl+TwJMBdyOCoAD6aN4GSqjpeWpJldDlCNKuobeDiv69i9d4SnpkzghmDZf/LjpBBJhcTG+yNn6cbh8trjS5FiGbDn/gegH/OTeM8WfK4w6QH7mI+XJ9HcWU9UQFeFB6TEBfGO9RiOG/WEOl5d4YEuIu5eEQMYX6efJJ5kHF/Wcov/99PvLc2l6o6uTpTGOOnPcUAfH/vVJSSy+U7QwLcxcSH+LDm4Rl8/bsp3DdzINX1Fh5duI2Jf13GD7sLjS5PuKD1+0sJ8nGnf7if0aU4HQlwF+RmNjEkJoDfnjOAb+6ewmd3TCA2yJub317PN1sPG12ecCHFlXV8teUwk/uHyUqDXSAB7uKUUqT1C+HTOyYwOCqAO97bIBsfi17z4bo8qhss3HvuQKNLcUoS4AIAHw83LhoRDdjWYBaiN6zJKSUl3I8UGT7pEglwQW2DhReXZPHikixig7y5dapcASd6R3yIN3kl1ewtqjS6FKckAe7i6hutzHltNS8uyeb8YdF89bvJDIz0N7os4SLunN6feouVxxZuo77RanQ5TkcC3MX96asdbD90jOcuH8Hfrx5NkI9cvix6T1ywN78cHcvPOSWs2iPLHHeWBLiLOlbbwB8XbeM/P+cyd3w/rkiPN7ok4YKUUgyPCwRs78OIzpEAd1GvLNvDO2tyuXZcAg9dMNjocoQLe/OnfUQHejEuKcToUpyOBLiLKjxWS3yID09fNhw/2bJKGOisxBAOl9fy6g97jC7F6UiAu6jqegve7rLbiTDeM3NGAPD891kcPFptcDXOpcMBrpQyK6U2KqUW279+Sim1RSm1SSn1vVIqpufKFN3NqrWsOyEMp7Vu7nnLzvOd15l/sbuBnS2+fk5rPUJrPQpYDPyhOwsTPSsu2If9xVVUyiJWwkBrckqb92hd/+hM4oJ9DK7IuXQowJVSccBFwL+bjmmtj7U4xRfQ3Vua6EkXj4yhpsHCV1sOGV2KcGFDYwMA8DCb2HKwzNhinFBHe+AvAg8CrWbaK6WeVkodAK6ljR64UmqeUipDKZVRVCTzPB3FmIQgUsJ9eW9tHlrL315hjAAvdzY+fi5xwd7MfWMdDy/YanRJTuW0Aa6Umg0Uaq0zT7xPa/2o1joeeA+461Tfr7V+XWudrrVODw8PP+OCRfdQSnHjpCS2HCxna3650eUIFxbs68Hi301mUv9QPliXx55Cuay+ozrSA58EXKKU2g98CMxQSr17wjnvA3O6uTbRw+KCvAGoqrMYXIlwdT4ebvzP+akA7CmsMLga53HaANdaP6y1jtNaJwJXAcu01tcppQa0OO0SYFcP1Sh6SHyI7Q2jD9fnyToUwnB+XrbrEYoq6w2uxHmcybydvyqltimltgCzsM1SEU4kKtCL2CBvFm06xIy/LWebDKUIA5VU1gG2HepFx3QqwLXWy7XWs+2352ith9mnEl6stc7vmRJFT/HzdGP576fz+tw0yqsbeGWZXAknjOPv5Q4g+7N2gsycd3HuZhOzhkYxNimEb7cfocEiQynCGIOi/Dl3SCSv/rCXzzIPGl2OU5AAFwAMjLKtAf6HRdsMrkS4sucvH8nYxBDu/2Qzb6zaJ1NcT0MCXABw78yBDI0JYO2+UqNLES4s0Med/9w8lllDInlq8Q6uf3Md+4qrjC7LYUmAC8C2DsV5Q6PIKaqSBYWEobzczbx2XRq3TUtmZXaxvCpshwS4aJaeGAzA5Gd+4ECphLgwjtmkKCivxaTgnpmyY31bJMBFs7GJIaT3s4X4hS+t5Nlvd8lVccIw1fUWYoK8SbP/TIqTSYCLZm5mEx/dNoEPbh1PanQA/1yRw4UvreSJL7aTW1JFeU2DvKkkekV9o5Udh4+RFOZrdCkOTbZiEa2YTYoJKaFMSJlAUUUdz367i3fX5PL26v0AxId4c87gSEbGB5IaHUBiqC9esjGE6GavLMvm4NEaJiSH8vnGfJSyrd+jAKXA1OL28eMKk7IfQ7W6z6TsX9vPa7ptMrXzOM2PYbttMkG/EF+8PRzn5131Zo8qPT1dZ2Rk9Nrzie5xuLyG77cXUF1vYe2+EtbklFDbYJsvrhTEBHqTHO5LSrgfSWG+pEYHMDI+EE83x/lBF85lxvPLyXHA2ScXDIvitevSev15lVKZWuv0k45LgIvOarRYySmuYveRCnKKqsgprmRfcRU5Rcc3iIgK8OK9W8eREu5ncLXCGVXUNlBaVY/Wto0GrFpjiyqNVYPWx49p7J/tt233azT2z02PYW061vJ+++O0Olfbj7d+nPmrc8krrean/5nR6/8ebQW4DKGITnMzmxgY6c/ASP9Wx7XWFFXUsSHvKL//ZAtPLd7B278ea1CVwpn5e7k3X1rvKPYUVvLz9yVU1TXi6yAbgcubmKLbKKWICPDi/GHR3D49heW7i1i4US6JFn1D/whbh2VvkePMzJIAFz3ixomJpPUL5t6PNvPN1sNGlyPEGRsQaRsOzC6QABd9nK+nG+/fOo6hMQHc89EmuTBIOL1+IT64mxXZDnRthGMM5Ig+ydPNzD+uS2PKsz8w5dkfuHVKUvNULQDs07PANpvFfqjFbdXqOPbpXabm6WKq+XbTNLHmr02qeTrY6c9veb/9mKmT57e839TF51QK1eJ7zSaFWSnM9rYIY7mZTSSH+TnUjkES4KJH+bSYM/vumjxs7+nTPDPA9kXTp6aZBjS/89/iblz5GqLmQLeHusmkcLN/bVK220oprFrbP2yzLppvN82waPFv2nLGRdOxZrrlzeNfaH3KU5o1zblG0Wr+ddO8bdTJx1rNyz7FfO1TPfeJWs6m062On3BeB9py8vMcP1Be00CD1XGWXJYAFz0q1M+T/X+9qNseT7cIpKZQahVSVlqF2GnPb3m/lRPO6cRjWOnaczadb239PVYNFqtu/rBqTaPVdl7jKY5ZrBqL1q167E2hb7KnYMtwhNYXrzQdbPH6qFV4tuz/tz5+/Ivm6XzY2sEJ0/eOTwVs/e/ccgrf8amBx6f70cbzdbTGdtt10gub07d/dILjXNovAS6cilIKswIzJ/3mCeFy5E1MIYRwUhLgQgjhpCTAhRDCSUmACyGEk5IAF0IIJyUBLoQQTkoCXAghnJQEuBBCOKle3dBBKVUE5Hbg1DCguIfL6Wl9oQ3QN9rRF9oA0g5H0ttt6Ke1Dj/xYK8GeEcppTJOtfuEM+kLbYC+0Y6+0AaQdjgSR2mDDKEIIYSTkgAXQggn5agB/rrRBXSDvtAG6Bvt6AttAGmHI3GINjjkGLgQQojTc9QeuBBCiNOQABdCCCflMAGulBqplPpZKbVVKfWlUirAfnysUmqT/WOzUuoyo2ttTzvtOFcplWk/nqmUmmF0rW1ppw2hSqkflFKVSqlXjK7zdNpqh/2+h5VSe5RSu5VS5xlZ5+kopUYppdbYfwcylFJj7cc9lFJv2du3WSk13dhK29ZOG9yVUvPtbdiplHrY6Frb0047rm2RU5uUUlal1KgeL0jbt3gy+gNYD0yz374JeMp+2wdws9+OBgqbvnbEj3baMRqIsd8eBuQbXWsX2uALTAZuB14xus4zaMcQYDPgCSQBewGz0fW2047vgQvsty8Elttv/wZ4y347AsgETEbX28k2XAN8aL/tA+wHEo2ut7PtOOGc4UBOb9TjMD1wYBCwwn77v8AcAK11tda60X7ci1PvpepI2mrHRq31Ifvx7YCXUsrTgPo6oq02VGmtVwG1RhXWSadsB3ApttCo01rvA/YAYw2or6M00PTqIRBo+jkaAiwF0FoXAmWA4ReXtKGtNmjAVynlBngD9cCx3i+vw9pqR0tXAx/0RjGOFODbgEvst68A4pvuUEqNU0ptB7YCt7cIdEfUZjtamANs1FrX9VpVndORNjiDttoRCxxocd5B+zFHdQ/wnFLqAPA80DTMsBm4VCnlppRKAtJw3P+rezh1Gz4FqoDDQB7wvNa61JAKO+YeTt2Oln5FLwV4r25qrJRaAkSd4q5Hsb3EfVkp9QfgC2x/iQHQWq8FhiqlUoH5SqlvtNaG9QK72g779w4FngFm9XSd7TmTNjiSLrbjVDsiG/rK7jTtOAe4V2v9mVLqSuANYCbwJpAKZGBbY2g1YFjnpottGAtYgBggGFiplFqitc7ppbJP0sV2NH3vOKBaa72tV4o1ekypjXGmgcC6Nu77AUg3usautAOIA7KASUbXdib/F8CNOMEYeFvtwNZrerjFfd8BE4yusZ3ayzl+zYYCjrVx3mpgiNH1dqYNwKvA3BbnvQlcaXS9Xf2/AF4AHumtehxmCEUpFWH/bAIeA/5h/zrJPj6GUqoftnHN/QaVeVrttCMI+ApbcPxkWIEd0FYbnE077fgCuEop5WkfehgArDOmyg45BEyz354BZAMopXyUUr722+cCjVrrHcaUeFqnbAO2YZMZysYXGA/sMqC+jmqrHU0/Z1cAH/ZaNUb/RWvxl+tubL3TLOCvHP8rNxfbm36bgA3AL4yutYvteAzbWN+mFh8RRtfbmTbY79sPlAKV2MaOHbLH14F2PIpt9slu7LMKHPUD28yfTGxj3muBNPvxRHv9O4El2JYcNbzeTrbBD/jE/ju+A/i90bV2pR32+6YDa3qzHrmUXgghnJTDDKEIIYToHAlwIYRwUhLgQgjhpCTAhRDCSUmACyGEk5IAF0IIJyUBLoQQTur/A7DRiGxCtTiUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#arrghh!  - included the lakeshore tracts\n",
    "for lT in range(len(lakeTracts)):\n",
    "    t = lakeTracts[lT]\n",
    "    isSkippedTract[t]=1\n",
    "    tractMAP = tractMAP.difference(tractGeom[t])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a21ae01b-8e2b-476a-8e19-71cd2da83d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tractMAP = tractMAP.difference(tractGeom[965])\n",
    "isSkippedTract[965] = 1\n",
    "plotPoly(tractMAP)\n",
    "wholeMAP = tractMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "43fe23ef-5a42-4854-b70a-789511d823ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(tractMAP)\n",
    "plotPoly(clipPoly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  15 tracts w pop 39590.0  to reassign to tracts...\n",
      "[70, 73, 94, 264, 266, 1178]\n",
      "I have flagged  84 precincts to reassign to precincts...\n",
      "[247, 250, 251, 252, 253, 297, 298, 299, 329, 330, 362, 363, 364, 365, 369, 2622, 2623, 2624, 2625, 2628, 2629, 2630, 2631, 2632, 2633, 2635, 2636, 2637, 2638, 2639, 2640, 2641, 2642, 2643, 2644, 2645, 2651, 2652, 2989, 3000, 3206, 3207, 3208, 3212, 3213, 3214, 3215, 3216, 3217, 3218, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3238, 3239, 3240, 3241, 3242, 3253, 3992, 3994, 3995, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4024, 4025]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1  #uncomment once confirmed\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1  #uncomment once confirmed\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.2 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  39590.0 people (VAP of 32883.0 ) and  26408.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iXC3AUw4d55bXUyipqgMgKsCTnJJqQnw8SHn4XPkHLVya1prdOaV8tDWbF9YeaPXYSf2DuXpiHBePjaGwvIafvbuN2no7ZpPimwzHYhhJUf7869pxmJSSu01dzJkG+HvAE4Af8HNngG8FPgLOB6qd2ze19jpGB/je3DLWHyikX7AXCsXdS7fgZ7PwyPwkDh6rJCO/jLoGzYLR0cxJijCsTiE6qr7Bzo6sEnZmlfDFrlwKy2u5bko8O7NKSD1cRHpe+WmflxDiTW29ncRw36YgB4gP8ea6yfEMjvDj8505RPp7cc2kOML8bD3VJNFMpwNcKTUPuFBrvVgpNYsTAb4TWAXcA0wA3gYG6FNeUCm1CFgEEBcXN/7w4cNd0JyO+++GIzz8vx1NV5c0WvKTSUwbGGpITUL0lG/3HWPhSxuavr92chz/d/5QfG2Wpk+a76Vm8lVaHj42CzsyS9ibV3bSa1hMCk+rmd8uGE5MkBcZeWVU19n5yYz+lNXU42E24Wk192i7+oozCfAngOtwnKj0BPyBD4BQ4I9a69XO4/YDk7XWBS29Vk/2wL/em09uSTUbDx5n85EiDhdWMiTCjxevT6agvJr0vHLiQ7yZmijhLcTp7MsvI7ekhpExARRW1PCTN1I4UFDxg+PMJoVda7ysZs4fEcm0xFDmjoqSMO9CZzSE0uxFZnGiB347EK21fkQpNRj4Cog7tQfeXHcHuNaaT7bn8OSXezlyvBKAYB8PJiQEMSEhmIvHxhDqKx8BheiMmvoGjhRWYteQVVxJYpgvK3bnkVlUhY/NTH5pDe9tzmy6iznEx4PXbprIyNgAYwvvBVoK8DO5Ru4V4BXnUEotcENr4d2dtNZ8sTOXf3y9j13ZpQR6W7llen8uGRvD0Eg/LHIViRBnzGYxMyjCD4AhkY4/fzJjwEnH/PnyUbzx/WEeX5ZGYUUtn+7IlgDvRh3qgZ+p7uqB/2NVBk8uTycxzIcbpibw4+R+8vFNCANtOVLEJf/8DoBFMwfwqwuHGVyRe+uOHrhL+H5/Ic+szODcYRH8+7rxMmeEEC7AZjnRgSp1XqYrup5bB/ja9AKuf2UjHmYTv7xgqIS3EC7gX6v386cv9hDi48FfrhjF2UPCjS6p13LrAE/LKQXg4XnDGBjua3A1QgiAz3fmANA/1IeRMYHYNWw9cpwDBRXkl9UwLMqP2UPlPouu4LYBXlJVx7NfZTAw3Jfzh0caXY4QwumlG5J56MOdrNidx4THT3+j9id3TmdkbIDcwn+G3PIk5oYDhfzm413szSvjkzunMyJGznIL4WpSDx9ne2YJpVX12LXm4rExvP7dIV777hAAHmYTdXY7NouJhBAf5o+OZvGsRAn003D7k5gVNfX8e+0Blu3IYV9+OZH+nrxy4wQJbyFc1OnWFn10wXDmjopi1Z58aursAGTkl/FNxjH25O6lqraBRWcNwN/TakTJbsdteuB/+mIP/1q9n1GxAVw0JoaFk+LkUkEheomKmnque3kDm48UAzA+Poh3bpsiFyY4tdQDd5s7XP7z/WFGxPjz8Z3TuWV6fwlvIXoRH5uF9++YyoeLpxIT6EXq4SIufu5bqusajC7NpblFgOeXVlNZW8+cYXKyUojeSilFWXU9WcVVgGNoRQK8dW4R4CvT8rFrmDtKAlyI3qyk2U0/a35xNoHestJQa9wiwBv/UmV5MyF6t3mjorj33EEAzP/7OgrKagyuyLW5RYAHeTvOSGcWVdH8pKtBc2cJIbqJUop7zx3Mwklx5JfVsP5AodEluTS36NKOjA3ApOCK578HYESMP5W1DRw9Xsno2EB+MqM/c5Ii5Yy1EG7ujjdTmxYUt5gUkweEGFyRa3OLHvjw6ADeunUyk/o7rik1m0wMDPNl4aR48sqquf3NzVz6r++wn7rcjhDCrfg2GyZ98fpkWcKtDW7RAweYNCCEt2+b8oPtv56XxDUvrmfDweMUV9UR7CMnPYRwV3++fBQ2q4k31x9hUITMb9QWt+iBt8ZsUvzsvCF4WExc8+J6CsvlpIcQ7kopxSVjYwHHGp2idW4f4AAT+wfz8g3JHCqs4Pxnv+GVdQepqKk3uiwhRAel5ZTyzMp0AGYPlWlo2+I2QyhtmTEojLduncyvPtzJ7z7dzcaDx3n+uvFGlyWEaAe7XfPHL/bw4jcH8LKa+f3FIxgVG2h0Wby5/jDvOj8JKMCkHJ8STAoUChRNj5UCk3L8qZRCQdM2H5uF3y4Y3uVDvL0mwAHGxgXx+T0zuPm1TaTllhpdjhCinbZnlfDC2gNMHhDMPxeOd4lzWe+mHOXh/+0kKcqfMD8bdudly1qDXeumP+0atLajNWhO7NNao4HymnoOFFRw+fhYzhoc1qU19qoAB9h8pIg9OaV4eshcKUK4C5vF5PzTfNrwLiir4dPt2cQEejEnKaLbp5xdtSePX36wgxmDQnn5hgl4WDo/2vz13nxuenUT3VFxrwlwrTXvpmbywHvbCfK28ruLRhhdkhCiHSpr67ng2W8AODfp5JV66hrsLN10lMc+3U1tvWP62bOHhPHPhePx6qZO2pYjRSxespmkKH/+de34MwvvPfncvXQLgd5Whkb6dWGVDr0mwNdmHOOB97YT4W/jg8XTiAn0MrokIUQ7PLsyo+nxox/v4q/L91JTbyfAy0pOSTUAMwaF8vDcJD7YnMm/1x7gn6v38bPzhnR5Lct25PDgBzuIcK434NvJ6Tu01jz39T6eWpHOsEh//n3deML9Pbu42l4S4A12zRfOdfj+cMlICW8h3MiPJ/Qju6SaT7Zl02DXTB4QQlSAFwXlNdQ32JmaGMI1k+IxmxTzR0fz77UHOHCsoktrKCir4ZGPdvL5zlxGxgTw3DXjOn0TUXlNPT9/Zxtf7MrlojHR/PHSUd32acGtA1xrzZe78vjX6n1syyzh0nExzOzikwRCiO6VGObL368ey7NXjkFDi1NiLN+Vy6L/pBLhb+M385K65GdrrfloazaPfrKLypoGHjh/CItmDMBi7tywSV5pNde/vJF9BeU8PHcYt0zv363j9W4d4Pvyy7n9zVQAnrlyDBeNiZb19IRwU6ZW5jLKLKpk0X9S8bNZeOe2KV0yHJFbUs1DH+7gqz35jI0L5C+Xj2JgeOfHqY8er2ThSxsoLK/h9ZsmMn1Q6BnX2Ba3DvBQX8dHnEvHxXDx2BiDqxFCdJdznloDwNu3TSE+xOeMXquuwc57qZn84bM06ux2fj0viRunJpzRZHgHj1VwzYvrqaxt4M2fTGJsXNAZ1dhebh3g9c7JqxLDZM4EIXqrT7ZlU1Nv55yh4SRF+3fqNbTW7Mgq4YPNWXyyLZvCilom9Q/mT5eNIiH0zN4Qjh6v5JoX11Nbb2fposkMi+pcjZ3h1gFeVu1Y6GF/frnBlQghukNZdR1PLEsD4C9XjO7Ua3yVlscflqWxv6ACD7OJc4aFc+m4WM4ZGt7qsE17ZBVXcdUL66mqa+CtW3s2vMHNA3zZDseVJ+cMi2jjSCGEO1p/4DjZJdX8aHhEp+7OtNs1D/9vJ55WM09cOpILR0QR4Fwg5kzllVZzzYvrKa2u478/6fnwBjefzGruqGh8bRbeSz1qdClCiG4weUAwAV5WVu3J59GPd3G4sGOXD246dJyckmruPXcQV0+M67LwLiir4ZoX13OsrIbXb57IyNiALnndjnLrAO8f6sPYuEDS88pleTUheiE/Tyuf3jWd+aOieeP7Q8x5ei1pOe2f5+ijbdl4Wc3MSeq6T+nHK2q59qUNZBdX8+pNExnXQycsT8etA9xu19gsZrKKq9h6tNjocoQQ3aBfsDd/vXIMy++bCRouePYbfvHuNl799iCph4tIzyuj/DTTR9fW2/lsew5zkiLw9ui60eKnV6Rz8FgFL9+QzETnKmFGcdsx8F3ZJTzy0S5SDxcxKjaAAXIlihC92sBwP167aQKPfrKLVXvym6Z5bRTgZSUm0IuYIC/ig70ZGO5LSVUdl4zr2kuMjxyvZGiUH1MHdv913m1pd4ArpcxACpCltZ6nlHoUuBUocB7yK631sq4v8YcOHavg4ue+xcNs4s+Xj+LycbFnfDZZCOH6pg4MZfl9Z1Fbb2f0b5dTVdfQtK+kqo6Sqjp2O4dY/GwWQnw8mNHFQVtQVkNkQNfPa9IZHemB3wOkAc1PtT6ttX6ya0tqH9VsckYJbyH6lg82Z1JV18DDc4dRVdvA/oJy9hdUsL+gnMraBiwmRVVdA5eOi+n0bfGnU9dgJ6+0mhExPX/Fyem0K8CVUrHAXOBx4P5uragdEkJ9ePu2ydz46iYeeG87UxNDiA3yNrosIUQPOV5ZC8C1k+PxtJr5Ymcu8SHeDI30I7Ooihtf3Uh+WQ2Lzkrssp/ZYNfc9/ZWCitqmTXENZZ7a28P/BngAeDUiQLuVEpdj2No5Wda66IurK1VY+OCOH94JMt35xIdILMPCtGXeDh71TX1dp5fs59nVmZgNilumzkApWB/QQXPXjWmy2Ymtds1D36wnU+35/CrC4dy4cioLnndM9VmgCul5gH5WutUpdSsZrv+BTyGYxWhx4CngJtP8/xFwCKAuLi4M6+4mcq6BqrqGigoryGiG+baFUIY53hFLa9/d4jMoipySqrIKammqLIWu11T7Vzc4c9f7GHJhiNcOi4Gs1L8c/V+AC4eE81FY7rm5KXWmsc+2807KZncPXsgi2Z2Xa/+TKm2rp9WSj0BXAfUA544xsA/0Fpf2+yYBOBTrXWry+AkJyfrlJSUM625SerhIq5+YT2Xjovhj5eN6rLXFUIYb8mGwzz04U4i/G3EBHoRFehFsLcHZpPCpBSvf3+IBrvm6on9ePzikZhMijXpBXyxM4cHLxyGv2fnbtrJyCvji525gGNR4oPHKnl/cyY3TUvgkXlJhsx4qpRK1Vonn7q9zR641vpB4EHni8wCfq61vlYpFaW1znEedgmws+vKbZ9RsQGE+HpQ5BwPE0L0HnbnZHWf3T2jaeZRcPSIf/fpbhrsmuunxPPo/OFNFzKcNTis0wsH19Q38PHWbH7x3vYf7Fs4KY5fzzUmvFtzJteB/1kpNQbHEMoh4LauKKgjfrpkMzkl1dw3Z3BP/2ghhAHsds0jH+/kzfVHuHlaf349b1iXheqjH+/irY1HiQ/x5v45g7lwZBSNAxRnsi5md+pQgGutVwOrnY+v64Z6OiSv1LFe3gUjIg2uRAjRXRpD1G7X/OrDHSzddJTbzhrAL88f2mXhXVJVx+5sx/Xjvzx/KBe4yEnKtrjtnZjgmAc8LbcMm6V71psTQriGBrvm/97fznupmdw1eyD3zxncJeG9LuMY/167n+/3F1Jv18QGeTE+wbi5TTrKrQP8aFElVpPCanatcSkhRBdwBnSDXXP/O1v5aGs2988ZzN3nDOqSly+qqGXxklR8bRZumdGf85IiGNMv6IxW5ulpbhvg2zOL2XSoiHvPHeRyJxaEEF3n5+9uY92+Yzxw/hAWzxrYJa+ZllPK7W+mUlXXwNJFUzq90o/R3DLAC8pquOPNzfjaLFw/JcHocoQQ3WjdvmM8dOEwbp05oEteL7+0mkv/+R02q4mliya7bXiDmwb4u6lHySqu4oXrxndqlQ4hhOvztTnObT06P4kbp/Xvstf90xd7qapr4NWbJjA+3tjpYM+UWwb4sEh/PMwm7liymamJIcwfFc2CMdF4WuVkphC9xfxR0YzpF0T/M1x0uLld2SW8vzmT22YOYPKAkC57XaO45sWNbTh7aDhf3jeT22YO4HBhJQ+8v53zn1nL2vSCtp8shHALFrOpS8Nba80zKzPw9jB32Vi60dwywMGxnNoD5w9lzS9m8cbNEzEpxfWvbOTO/25uuj5cCCEaPb0inRW787jnnEFdtjam0dw2wBsppZg5OIzP753B/XMGs3x3HjP+9DX3LN3Cntz2r50nhOi9dmSW8LdV+7hsXCyLuuhkqCtw+wBvZLOYufucQay4byZXT+zHyt15XPLcd+SUVBldmhDCQGk5pdz8+ibC/WyGTUbVXXpNgDeKD/HhtxeN4HcXjaCqroErnv+ehz7cIePjQvQxWmteXHuABf9Yh9aaJT+Z1GuGThr1ugBv9KMRkdw8rT8Wk2LJhiPc8OpG3tp4hO/3F1JaXWd0eUKIbrZqTz6PL0tjSmIoX9w7k0ERp65H4/7anA+8K3X1fODt0WDXFFfWcs5f11BceSK4vT3MJEX5k5wQTK5zmMWkFDarmcQwH+aPjpZFIoRwU1W1DZz71zV4Wk18fs9Ml51NsL06PR+4uzObFCG+Nl69cQLf7S8kMcyH3dmllFbXs/HgcZ5fs58IfxueVjMNdk1lbQPHK2p5cvle/nvrZMbFuc/ENkIIh/+sP0RWcRVvL5rs9uHdml4f4I3GxgUx1hnG5484MVVkSWUd/l6Wk05sHCgoZ+FLG/j5u9tY9bNZPV2qEOIMVNbW8/yaA8wYFMqkXnCzTmt671tTOwV4W39wVnpAmC9XT4zjQEEFa+TkpxBuZcOB4xyvqOXayfFGl9Lt+nyAt+S6yfEMjfTjhlc2cvt/UtmZVWJ0SUKIdliy4QhB3lZmDurc0mrupM8MoXRUkI8HHyyeyj+/3s+bGw6z4rk8fjQ8gpExgQwM92VguC9xwd5uNXewEL1dTkkVK9PyuHv2QLw8ev/cSBLgrfD2sPDzHw3h1hkDeParDJbtyGHZjtym/R4WExMSgjhnaAQT+wcTH+KNXydXwhZCnLlv9xUCjvmS+oJefxlhVyutrmN/fjkZ+eWk55axfHceR45XAuBhNnHJ2BiuSI5lfHxQr7rjSwhXZ7drZvz5awK8rHxy1/Re9em4z15G2NX8Pa0nXdHy8LwksourSDlcxLcZx/hkezZvpxylf6gPMweFsnByPIN74Q0EQria/QXlZBVXcdtZA3pVeLdGeuBdrKKmnmU7cvhgcxZbjhahNdw/ZzALJ8fja5P3SyG6yw2vbGTDwUJW3HcW/YK9jS6nS7XUA5erULqYj83CFcn9eGvRZL55YDZTEkN44vM9LHxpAw32nnuzFKIv2ZFZwpr0Am6a1r/XhXdrJMC7UZifjddumsiFIyPZdrSYrUeLjS5JiF7pwLFyAC5sdpNeXyAB3gO8rI6hk2dWplPfYDe4GiF6n/HxQXiYTTy/dr/RpfQoCfAe8NjFw7l6YhzfZBzj423ZRpcjRK8TG+TNXbMH8tn2HFbszjO6nB4jAd4DvD0s/OJHQwD4JuOYwdUI0TvddlYiQyP9+M1HO7H3kfNNEuA9JNDLysT+wXy4JYtv90mIC9HVPCwm5o2KIrukmpr6vjFUKQHeQ0wmxROXjiTI28rClzaw4B/r2JUt86sI0VVq6+2sSS8g0t+zT9xGDxLgPSoxzJd1/zeb3100nPzSGi557jsWL0ll+a5cucRQiDOwN7eMG17ZyKZDRdx/3mCjy+kxcmdJD/OxWbh+SgJzR0bxt68y+GxHLst25BLqa2NopB+zh4Zz49QETH3kTjIhOquoopa3U47y6fZsdmaV4mez8OfLRvHj5H5Gl9Zj5E5Mg9U32FmZlscXO3PZlV1KRn4580ZF8dDcYUQFeBldnhAux27X/OnLPbz27SFq6u2MiwvknGERXDMxjiAfD6PL6xYt3YkpAe5CGuyaC55dS3peOTaLiT9fPoqLxsQYXZYQLuXjbdnc/dYW5o2K4s7ZAxka6W90Sd3ujG+lV0qZlVJblFKfnrL950oprZQK7YpC+zKzSfHlvTNZef9MRsYEcM/Srdz06kbWpBf0mcuihGjL13vy8bVZeObKMX0ivFvTkZOY9wBpzTcopfoBc4AjXVlUX6aUYmC4H/+5ZRI3T+vPjqwSbnhlI7e+kcKXu3JJzyujvKaenvzkJISrKK6sZdOh40QFeGIxyzUY7TqJqZSKBeYCjwP3N9v1NPAA8FHXl9a3eXmYeWR+Eg+cP4RXvz3EU8v38tWe/Kb9nlYTk/qHMDImgMGRfkxLDCHE12ZgxUJ0rwa75q63tpBfWsPbt002uhyX0N6rUJ7BEdRNE1srpRYAWVrrbbJwQffxtJq5Y1Yi10+JJyO/nMOFFeSUVJNbUs3KtLymRZd9bRZev3ki4+ODDK5YiO7x1sYjfJNxjD9cMrJpPv6+rs0AV0rNA/K11qlKqVnObd7AQ8B57Xj+ImARQFxc3JnU2qf52CyM6RfImH6BTdseXTCcugY7O7JKuHPJZq57eQNPXDqSBaOjZTUg0eu8l5rJiBh/rp7Ydy4TbEt7BpGmAQuUUoeApcBs4D9Af2Cbc3sssFkpFXnqk7XWL2itk7XWyWFhvX+V6J5mNZsYFxfEhz+dxoAwH+5ZupWrXlhPbkm10aUJ0WVe/+4QW48WM2dYpHROmmkzwLXWD2qtY7XWCcBVwCqt9WVa63CtdYJzeyYwTmud29prie4T4e/JRz+dzh8uGcnWo8Xc9dZmKmrqjS5LiDOmteaZlekkxwdxx6xEo8txKXIatxcxmxTXTIrjtwuGk3q4iIuf+5YtR4qMLkuIM7Ijq4SiyjqunNAPD4tEVnMd+m1orVdrreedZnuC1lqm2HMRV02M48Xrkymvqefm1zZRUFZjdElCdNqK3XmYTYpzh0UYXYrLkbezXuqcYRE8t3AcFbUNnPf0GlIOHTe6JCE6pbquAbNJ9ZkZBjtCArwXGxcXxKd3TcfP08r/vb9dbv4Rbik+xIfaert8kjwNCfBebnCEH3eePZD9BRUs70NLTYneIynacbv8d/tllPZUEuB9wCXjYhgQ6sM9S7ewPbPY6HKE6JCx/QIZFuXPE5/v4XBhhdHluBQJ8D7Aajbx07MHUl1nJyOv3OhyhOgQpRT/XDiO4so6XvvukNHluBQJ8D4i5fBxx5qBo6OMLkWIDmtcfnBYH5998FQS4H1AQVkNb286yo+GR2KzyJl84X68nVegxAbLIifNSYD3AWk5pdg1/Dg51uhShOiUIc6e9/4CGQNvTgK8DzjkPPET4iPTzQr3U99g5/ef7gZgWKRfG0f3LRLgfcA459SbX+6SqWqEe2mwa3727jY+35nLI/OSSE4INroklyIB3geYnSvcbz5SJDfzCLehteZXH+zgo63ZPHD+EG6e3t/oklyOBHgfMDDclwWjo/km4xh3vrVFQly4hXdSjvJ2ylHumj2QxbMGGl2OS5IA7wOsZhPPXjWGqYkhfLY9h4raBqNLEqJVuSXV/P7TNCYPCOa+cwcbXY7LkgDvI5RSpOeVEehtxdfW3pX0hDDGja9upKymnl/PS8JkkgUcWiIB3odcPCaG4so6/roi3ehShGhVTkk18SHeJEXJjTutkQDvQx68cBhJUf58lSaTWgnX1XiOZmpiqCyf1gYJ8D6kqLKWkqo65BymcGXvpBylpKqOUbEBRpfi8iTA+5Dlu/LIKq5iRIx8LBWuKS2nlN99spspA0K4MllWn2+LBHgfsmBMNBP7B/NuaiYrZG5w4WK+31/IVS+sx8/TylM/Hi0nL9tBArwP8bVZeOPmiUT4ebJ04xGjyxGiydMr0rn6xfWE+dl49/YpRAfKpFXtIQHex2w+XERuaTUhvh5GlyIEAHa75tmvMgD4762T6BfsbXBF7kMCvI856JzYKuVQEX9dkc7OrBK5M1MYavORIgB+ecFQwv08Da7GvcgdHX3Mj5P7kVtSzbf7jvGPVRn87asMBkf4csPUBK6aENc0b4oQPWXDweOA49+m6BgJ8D7Gajbxs/OG8LPzhlBYXsPy3Xks2XCYhz7cyVdp+fz96rH4yJ2aoodorVm9N5/BEb4E+8iwXkfJEEofFuJr4+qJcXxy53Qeu3gEa9ILuEsmuxI9aN2+Y2w6VMQV46X33RkS4AKlFNdNjufaSXGs2pPPU8vlVnvRMz7ckkWAl5UbpiYYXYpbkgAXAByvqOXAMccJTi8PWTdT9Izs4ipCfT2wmuXcS2dIgAs+3JLJ7KdW8/3+Qn514VDuOCvR6JJEHzF/dDT7CypYtkNWi+oMCfA+bsOBQn72zjZig7z47O4ZLJqZKHfAiR5z6dhY/D0t3LN0C/sLyo0ux+1IgPdhB49VcNdbW0gI9WHJTyYzRBaMFT3My8PMYxePoN6u2Z1danQ5bkeuF+ujlu3I4e63tuBhMfG3q8cS4GU1uiTRR72TchQ/m4WZg8KMLsXtSID3UV+l5ePvZeWLe2fI3W/CUEePV1Fnt5NbWk2At3QkOkKGUPqo6roGgn08JLyF4f5+9Vhq6+3M//s6qutkvdaOaHeAK6XMSqktSqlPnd8/ppTarpTaqpRarpSK7r4yRVerrK3H0yrv38J4AV5W7BpqG+yk5cg4eEd05H/wPUBas+//orUepbUeA3wKPNKVhYnuFeBlJb+0Ru66FIbKK61m1pOrAfjTZSMZGxdkbEFupl0BrpSKBeYCLzVu01o3f6v0ASQJ3MiMQWHkl9Ww0TmRkBBGKK6sa3p8/ogoAytxT+3tgT8DPADYm29USj2ulDoKLER64G7lwpFR+NksvCULOwgDDYn048+XjcJsUiz4xzpWykpRHdJmgCul5gH5WuvUU/dprR/SWvcDlgB3tvD8RUqpFKVUSkFBwRkXLLqGl4eZy5Nj+XR7DtnFVUaXI/qwH0/ox+s3TaSgrIa73tpCSbNeuWhde3rg04AFSqlDwFJgtlLqzVOO+S9w2emerLV+QWudrLVODguT6zxdyczBYdTbNYcLK40uRfRx0weF8sJ1yVTVNZByWIb12qvNANdaP6i1jtVaJwBXAau01tcqpQY1O2wBsKebahTdJMJ5CeGu7BKDKxECQv0c84EXSw+83c7kRp4/KqWG4BgXPwzc3jUliZ4S7m/Dalb8/rM0ckuqueucQXJHpjCMxTkHz9Ei+UTYXh0KcK31amC18/Fph0yE+wj1tbHs7hn8c/V+Xlp3kIraBp64dKTRZYk+Kr+0BgBfWRGq3eROjj5uUIQfT185hkn9g1mzN9/ockQfNnlACEMi/Hh2ZYYM67WTBLgAHJdzZZdU8+LaA0aXIvook0nx9JVjsFnNXP3CejYdkpOZbZEAFwAsmjmAUF8b/9uaZXQpog9Livbnw8VTCfW1sfDFDTy1fC9VtTI/SkskwAUAsUHeXJEcy57cMkqr5SoAYZx+wd68f8dUZg0J4++r9nHXW1uMLsllSYCLJmP6BdJg11zy3LfS6xGGCvLxICrAcZnr3FGRBlfjuiTARZOZg8II87Oxv6CC859dy4dbMqU3LgyTVVxFbJAXl4yNNboUlyUBLpp4eZhZed9Z/GZ+EhU1Ddz39jbOeWoNy3bkUFRRKzMXih6jtSazqIowP5vRpbg0ueBSnCTA28pN0/qzcFI8W48W84v3trF4yWYArGZFcnww5wwLZ0y/QIZE+uHnKTf+iK6lteavK9LZk1vGXy4fZXQ5Lk0CXJyWh8XExP7BfHX/Waw/cJy9eWXkFFfx9d58fv/ZiWnhw/xs9A/1YUCoD/2dX0Mj/ekX7IVSsrq96Lgvduby91X7mD4wlMvHy/BJayTARassZhPTB4UyfVAoAA/PSyK3pJpd2SWk55Vz8Fg5B49VsGJ3HoUVtU3PGxblzys3JhMV4GVU6cJNLdlwBD9PCy/dkCydgDZIgIsOiwzwJDLAk3OGRZy0vaSyjgPHytl6tJg/fr6H33+WxnPXjDOoSuGuMosqOXtIOJ5Ws9GluDwJcNFlArytjI0LYmxcEMfKa3ju6/1cOjbvB0EvRGu8PSwyR307yVUoolssmpHI0Eg/bnk9ha9ljhXRASNjAjhyXGYkbA8JcNEtArytvH/HVAaG+/LTJZvJkh6VaCezWWGXS1bbRYZQRLfxsVl48fpkzn5yNdP+uIrFsxIBUAoUqtlj5wMcj0+3v/FcVvOTWo3HNe07zXNPHKtOfp3Gbc2+RylMCkzOY03O/af+aTYpLCaFSSksZuefJhMmE1hMJswmTrvNbDJhVgqzWWFWCpPzOLNyvIYyceKx8+eYnDX1pZN5NouJytoGtNZ9qt2dIQEuupWf54l/Yi9+cwCtobFvpbVGA9LZah9HoDvC3KwcbyIWs8JiNmExKbQGe7Pf6Ynfr8be7HucfwfNf/+ak/8Smv+dnLTnlL+rU58HzjfQxjdmTn6jbXrjdBx48puhyfHmdvS449Pa5iPFjI8P6vTvqy+QABfdKtTXxqE/zm338VrrpvBoHjKOfScCo+mYpmP1ad8cmo455bmNr33i5zj3a7A3C0K7XZ8UjA12jV1r6hscfzbYNfWnbKu3a+zO7U3HN9vWtE9rR7jaddPPtDvDtsGum4LXfsoxDfrEa9Q3aOrtduobdFPPXamTP3GYmh43/1SjTvp00xSqzTX/BNPsm1M7xc2//cEbQvM3C33y/sY3mcbfU+PvZkR0A14eZmKD5BLUtkiAC5fSfFjDucWoUoRweXISUwgh3JQEuBBCuCkJcCGEcFMS4EII4aYkwIUQwk1JgAshhJuSABdCCDclAS6EEG5K9eQ6h0qpAuBwOw4NBY51czndrTe0AXpHO3pDG0Da4Up6ug3xWuuwUzf2aIC3l1IqRWudbHQdZ6I3tAF6Rzt6QxtA2uFKXKUNMoQihBBuSgJcCCHclKsG+AtGF9AFekMboHe0oze0AaQdrsQl2uCSY+BCCCHa5qo9cCGEEG2QABdCCDflMgGulBqtlPpeKbVDKfWJUsrfuX2iUmqr82ubUuoSo2ttTSvtmKOUSnVuT1VKzTa61pa00oYQpdTXSqlypdQ/jK6zLS21w7nvQaXUPqXUXqXUj4yssy1KqTFKqfXO/wMpSqmJzu0eSqlXne3bppSaZWylLWulDVal1OvONqQppR40utbWtNKOhc1yaqtSyq6UGtPtBWnnsk5GfwGbgLOcj28GHnM+9gYszsdRQH7j96741Uo7xgLRzscjgCyja+1EG3yA6cDtwD+MrvMM2pEEbANsQH9gP2A2ut5W2rEcuMD5+EJgtfPxT4FXnY/DgVTAZHS9HWzDNcBS52Nv4BCQYHS9HW3HKceMBA70RD0u0wMHhgBrnY9XAJcBaK0rtdb1zu2e/GBZVZfTUju2aK2zndt3AZ5KKZsB9bVHS22o0FqvA6qNKqyDTtsO4CIcoVGjtT4I7AMmGlBfe2mg8dNDAND47ygJ+ApAa50PFAOG31zSgpbaoAEfpZQF8AJqgdKeL6/dWmpHc1cDb/VEMa4U4DuBBc7HVwD9GncopSYppXYBO4DbmwW6K2qxHc1cBmzRWtf0WFUd0542uIOW2hEDHG12XKZzm6u6F/iLUuoo8CTQOMywDbhIKWVRSvUHxuO6f1f3cvo2vAdUADnAEeBJrfVxQypsn3s5fTuau5IeCvAeXdRYKbUSiDzNrodwfMT9m1LqEeBjHO/EAGitNwDDlVLDgNeVUp9rrQ3rBXa2Hc7nDgf+BJzX3XW25kza4Eo62Y7TrZRs6Ce7NtpxDnCf1vp9pdSPgZeBc4FXgGFACo45hr4DDOvcdLINE4EGIBoIAr5RSq3UWh/oobJ/oJPtaHzuJKBSa72zR4o1ekyphXGmwcDGFvZ9DSQbXWNn2gHEAunANKNrO5O/C+BG3GAMvKV24Og1Pdhs35fAFKNrbKX2Ek7cs6GA0haO+w5IMrrejrQBeA64rtlxrwA/Nrrezv5dAE8Dv+qpelxmCEUpFe780wQ8DDzv/L6/c3wMpVQ8jnHNQwaV2aZW2hEIfIYjOL41rMB2aKkN7qaVdnwMXKWUsjmHHgYBG42psl2ygbOcj2cDGQBKKW+llI/z8RygXmu925gS23TaNuAYNpmtHHyAycAeA+prr5ba0fjv7ApgaY9VY/Q7WrN3rntw9E7TgT9y4l3uOhwn/bYCm4GLja61k+14GMdY39ZmX+FG19uRNjj3HQKOA+U4xo5dssfXjnY8hOPqk704rypw1S8cV/6k4hjz3gCMd25PcNafBqzEMeWo4fV2sA2+wLvO/+O7gV8YXWtn2uHcNwtY35P1yK30QgjhplxmCEUIIUTHSIALIYSbkgAXQgg3JQEuhBBuSgJcCCHclAS4EEK4KQlwIYRwU/8PRMqSU4a8i1kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now work on the northern protuberance\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-91.14,46.82)\n",
    "pt2 = Point(-90.3, 46.6)\n",
    "pt3 = Point(-90.3,47.2)\n",
    "pt4 = Point(-91,47.2)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "plotPoly(clipPoly)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  4 tracts w pop 6589.0  to reassign to tracts...\n",
      "[12, 148]\n",
      "I have flagged  11 precincts to reassign to precincts...\n",
      "[73, 155, 165, 187, 188, 189, 190, 191, 2203, 2213]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :# and tractGeom[t].centroid.y > 48.3:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1: # and vtdGeom[p].centroid.y > 48.3 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1 and vtdGeom[p].centroid.x > -123.4:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pqopIf+A+VT0XQERKgM8A1yRd2hRyXU1jjh6ClqbYRzRWze7dK1m2fCaCA+L4U0FwiEWiVK7ZzOKndqEKY8/4LOq63ksVVZdoOMLeHds45+vfpqR7j2xfkjEHjQ4FelV9E3jTnw8DV7SwzSbg3Cbva4DeyRQyHdKZo1es9tpcOLKbet3A1q1vAC6qLqD+1CUarYMuETQ4jPpdhSx45XlEHETEG5LRcajbsxuAqvXrGDxuQhavxpiDS6fNMLhuGgO9tt5ZWWflIhQVlDP5pPdaXD/v7VvZGZ3JBTf9nP5DT9xv/cali/nHz3/AMdMvtCBvTAd12oqnG9O0DQxiLUxa1uaXn/qPYMn+f5L1NTU8edvPKSrtwvGfvzg9hTMmj3XaGr26aQz0VqPfj4vgtPHlF1MvLYOGG5ZFwvUsfv0VXv/LTFDlzK9+nYJi6xLZmI7Kq0C/MxKlMhxFwH8cXxrmAX/eW7bVcamKxVi3rdpbJo3rAg6IODj4g2w4giPi/fwRCDjSMPiGt87ft0l0b+WRgk6rvS+/aM1SKIKdLyyi/zdOoXbvHp6e8Usqli+loLiEM758LaNOmZK5AhuTR/Iq0J/xwQoq6iOJbTwMGFbKLR+tSm0hVKFU6L0ntYc92GnD126jqqqVzJ37BcSJEiqqAWD3jiCfvD2fWf96mM2rlnP217/NkSefhhNIfZ9ExnQWeRXod0VjTOnVlYv69vL7XWnaB0vjvCpU765n++YaXPbdBvU67Ipv56L4ixu39ftz0abbNtlOgekjyrLwCeQul/0fmKqqWkJB4W62bRtAJNKPaKSQsXv7UfPMJrZVrOOML1/H6FPPyEp5jckneRXogyIMLS7k84f0bH/j/sDItBfJ+LSFHL3remMCdOmygznvX8CZI06mz4jusLKeKf0u48gzz85GUY3JO3nV6iYgQlSt74FcpC3ciB08+BQACgtrAGXAR0FYWQ9At4LeuHsSTMMZY9qUVzX6kEjDyEcmtyiNN6hVY1Rs+gcrVvy3t3J3fy4unULv04YT6lOCWx3BKQ7idA1lr8DG5JG8CvQB8QbmNrlIGn4+zn7/s9TUrAWgpGQ4R534EIXnp7/HUmM6q7wK9EERYhboc5QS3ruOFa+cRU1wbcPSE45/JYtlMqZzyKscfdBy9LnLhZiG+ZTVDYtOO3VRFgtkTOeRVzV6uxmbuxxXOJRhnHrqR/CbAd7CKfaUqzGZkGc1euxmbI5y449L1e7wFpzx02wWx5hOJc8CvdXoc5XGu5AIV3sLovXZLI4xnUpeBXpL3eQuN96OPlTsTde+nb3CGNPJ5FWOPuRYq5tUi8Wi/H7W0+yMxpI6TliG4ahCqd+McvOC5AtnjElIXgX6gEDU4nxKrdy4nF+5wwkSJYB7wMcJaZQju5TCype8Baf/MEUlNMa0J68CfVCEsGuRPpXCsQgg3F+2m7PGp6Cb4N+fBN0HwYTLkz+WMSYheZWjt5uxqReNeSmbgKSgm+DlL8CWxTDuIijplfzx0kTtb8jkmbwK9HYzNvWirhfoQ4EU/Kk8cqk3HTk1+WOlyd9mr+fYX7/G+m3V2S6KMSmTX6kbjbG7tppZS2ZRH4sRcaPUR6PEVHEEAuIQdBwCToCg41AYCNGrtBu9u/aiZ9cyQqGCFo+rrkskGiEYCOAE8uoja1ckFgUKCTopqNGPvxQWPgxlRyR/rBSrj8b4zQvL+cu76wB4afGnXHPq8OwWypgUyauo1fXT+awtGMGFlV06sFcUqAQq6RbdS69YNQLUS5A6CVHrFFDnFKD+oNVBN0qBRih0IxRolJBGcQAH9V/ekIXxIQwblquLqPpDEKq/Dw3DEzZ933wYRAdvuELx26ILTY+hTc7XdJk0rHNQ/5iN55AmZdhnXhqvwRFYXxeFgiNSE+j7TfACfSzc7qaZtLhiF+fdNWufZeVdC7NUGmNSL68C/S/2vslFn/yB0GdvpjAYIhQMEgqECAVCxNxYwyvqxoi5LjXhOrbXVrO9vpbt4SjbibHdP1YhUOS/ihEKxSEKhEWpB8IOhFUIq5fTdVVRdXFRXI2PaOWPQiWCKw4uDq7SZMSq+Etw8Z7qbfreRRpHv0IaX+KF9vi8i9MwBfxzScNURVD23a7hGM3216bZPAUKoDhWR7+ehyT/D7RnE963Wm4MC+i6yr1vf8ytLy1vWDasrJQzRx3CtPH9s1gyY1IrrwJ9zwCcopVw5AnZLkpuit+/aLiP0fR947yqel9Yrjd1nGCraa2EVa2EeX+BI86Bou7JHStJqsq7a7Yx4+UVLPhkJ4N7l7B+Ww0zrzyas0b3zWrZjEmHvAr0BArs0fq2+AN/NExb2wwI+K+UiEXgj1MgvBc+88tUHbXDPq7ay5PzK3htWSVLN++mb7ci/vfi8RzRtytT75yFtcw1+Sr/An3MAn3OqVwG4T1w1s1QdljGTlsXifHumq28sbyKd9Zs5eOqahyBiYN68svzx3DxpIEUBgMs/3Q34A8Ob0weyq9AHyzwao8mtyx8BBAY94WMnfLVpVu47sF5RF2lpCDAMUN68cXjB3PW6L7071G8z7YB/xeOBXqTr/Ir0DtBC/S5Zt07MPse6H04dMnccIFLN+0m6ip//o9jOPGw3hQGW09ExceyjVnuxuSp/Ar0sQgErVlcztizBf5yLkgAvvRiRk8dc11E4PSRfdrdNuDEBy1Pd6mMyY78CvTROgv0ueSRy7zpBTMzWpsHiLpK0Gn7pnNcfLNU1Og37azlww07+HD9Tj7csINVW/ZQFApwZL9uTJ/Qn8+N709RKDeal5rOI78CfaQOgkXZLoUB2PMpVMz15sdemPHTx1xtqKm3x/FTN498sIHZH29rWN487AtegyVHxHt4TRr3rdhZy9JNu/l0dx0ARSGHcQN7cNGkQ6mPxnj/4+1877GP+NXzy7jw6IFcc8ow+nSzv1WTGfkV6KMW6HNC1Up44quAwNVvZqUIXo0+sf55yrsWMnZAdyp21FKxo7ZhuTRrhuo9GAfqPxQXf++q0rdbEccN68XEQ3tw9OBejOzXdZ/+gVSV2R9v58H31/PXd9fxz7mfcOelEzntiPZTS8YkK+FALyIBYC5Qoarn+cuuB/4Trx+B51X1xhb26wHcB4zBqyR9WVXfS77oLbBAn33v3wuv/sQbSeqSh6D/hKwUoyM1+qJQgGevPzmt5RERThjemxOG9+bjqr3859/n85W/zuXn00ZzxfGD03puYzrSJeENwLL4GxE5HZgOjFPV0cDtrez3W+AlVR0JjG96jJSzHH12LXkSXrwRhkyGr8/Oai+VUddNOEefacPKu/DotSdw6ohyfvzUYm5+fimutfgxaZRQoBeRgcBUvJp53HXALapaD6CqlS3s1w04BfiTv01YVXcmWebWReutRp9NH/zJm178AHTNblcCHanRZ0OXwiAzrzyaq04YzB//vZbrHppHbTi54RqNaU2iNfo7gBthn7HkRgCTReR9EXlLRI5pYb9hQBXwZxGZLyL3iUhpSycQkatFZK6IzK2qqurAJTQRrYOQBfqsWfdvb/rWrdktBxCNJd7qJluCAYefTx/DTz83ileWbuGSme9Ruacu28UyeajdQC8i5wGVqjqv2aog0BM4Hvge8Kg0v3vlbXMU8HtVnQhUAz9o6TyqOlNVJ6nqpPLyA2yKZ61usmuwn+eu3ZHdcuDX6AO5HejjvnTSUGZeOYmVW/by+XveZcmmXdkukskzidToTwKmicg64BFgiog8CGwEnlDPHLzaflmzfTcCG1X1ff/9Y3iBPz0sR59dX3oeeh/mNa3Mso60uskFnxl1CI9ecwJR1+Xzv3uXGS8vZ3edPeVtUqPdVjeqehNwE4CInAZ8V1WvEJFrgSnAmyIyAigAtjbb91MR+UREjlDVFcAZwNLUXkIT4b3eDcEBkyBS470P13gdnYVKoKDUf3Xxav7BQgiEIFDodYgWLPCmAX95W+sPoiCSUbU7Ydtqfj3715w77Fwm9JmQlWLkeo6+JWMHdueFb07ml88t5Z431vDwnE/41pmHc+Xxg/dr6plxkTpY/hy8dRtEa+GLT0OvYdktk0lYMu3o7wfuF5HFQBi4SlVVRPoD96nquf521wMPiUgB8DHwpaRK3JaSMtizGV75kfc+UAgFJd40UusFfk3RDS8JtPFFUADSwheBiNcfjxPyBt9wgk1ezd8HIRBsZ5uW3odaX+8PNtIwJT4KiuvPu/6r+bJWtnNj3ufpxhrf12zlV8Ux/rHiYXTmgxR8eiijXng5NZ95B0Rdl9WVe/nWI/MbHnAKOPs/7NQwcld8mSMND0U5LTwcFd/+2KG9OGZI6gc4792lkDsumchXTh7Gb15cxk+eXkLFzlpuOufIlJ+rXbU7YfHjsGWJN63b2bhu5ulw6cMw+MTMl8t0mOTiiPeTJk3SuXPndnzHcA3U7fKCe6jUC5RNqXotc8J7vTRPtN7rHyfmT6P13jB38Vcy61v6XNUFN+q/Yk3mm76PtLPef+Wwaw8p553iIu75XYzy3XDk8vS1qG3NX99dx/3vrAW8B5pc13toKab7P+zkuv5IYE2WNb7XFvupHzOgG89dPzmt16Cq/PipxTz0/gb+cMVRnD2mX1rPt4/178ETX4Ndn3gVmSPPg4lXwtBTYcN78Ow3Ycd6OHcGTEpf3c0kTkTmqeqkFtflVaDvLOK166aBPxZt9sXQ/Msh4j/Tr01GlBLvl4fgT50my6SVZdK4zAl4v2wapo43DYQgVIyqZj/lkCJNvxiu+ds8Pt1Vxws3pDfQA4SjLhf+4V3Wba3mhRsmM7BnSWpPsG0N/OMKOPwsmHAZlI2A9+72HnrrMRjO/z0cetz+qcqa7fD4V2DN6zDhCph6u/eQnMmatgJ9fnWB0FmINAZYcvfmc74EefCuJSAQwLumTN2iKQg63HnJRM67axbfemQBj1x9PMFACk++bQ1ULvVe79zh/RKOVMOR02D6PVDUreX9SnrB5Y/Bm7+Bt2fA5gVw0V+g7PDUlc2kjN1RNKaDXNWGwUoyYUhZKTd/fgxz1+/gt6+tSvHR/V/0F/8Nzvs/OOqL8Lk7vYfeWgvycU4ApvwYLn8cdm+Ce0+BeX+1/p5zkNXojekgVzP/a2X6hAHMWrWVu99YzQnDenPiYc1bMiep2wAYNe3A9j38TLjuHXjyGi93v+oVmHaXV+s3OcECvTGtWLe1mp89u4RozGuqGX+9vbKKiYN6ZLw8P58+mnkbdnD9w/P5xzUncFifLskfNF77TvZ7q1t/uPJpL7//2i/gnuO8vP2o6UkX0STPUjfGtOLDDTt4c0UVK7bsYWdthMo9dXyyvYYBPYqZnOoadQJKCoLcfelR7K2PMv3uWTzx4caMl6FNjgMnfROufgO69YNHv+i9cuABus7OavTGtCL+wNUjVx/P8PIU1J5TYFT/brz0rVO48bGFfOfRhby9sopfnj+GrkWhJI+cwlRU37Hw1dfg3TvhzVtg9Wtw6vfhuGvsyfUssRq9Ma2Ijx6Va10IDy0r5eGvHc+3zxzBMws3ce6d/+bDDQfav1Cari0Qgsn/5XVXPfgkePW/4a5J8OED3jMnLdm+Fhb8HWb9H4Sr01OuTsoCvTGtiNfoYznYiiQYcLjhzMN59JoTcF246A/vcffrqw583Nt03VzuPRwufxSueBxKy+CZ6+HuSTDnj1C9Der3wMdvwUMXw50T4Knr4F8/g1d+nJ7ydFIW6I1pRbxGn4pBw9Nl0pBevHDDZM4d24/bX1nJ5ffNZvOu2vZ3jMvUl9hhZ8LXXodL/wFFPeCF78KMYfCbgfDANNj4AZz+I+8XQN9xsOjxzJSrk7BAb0w7JJX56zToXhzizksmMOPCcXy0cRdn3/FvXlrc0RugGbhGETjibG8c4csfa1z+mV/Ct5fAqTdCnyNhxGehflfrKR7TYXYz1pg8ICJcNOlQJg3pxQ2PzOfaB+dx2XGD+O+poyguCLSxZxZ+rYjA4Z+Bn7XS736Bf+N79b/giHMyV648ZjV6Y/LI0LJSHrv2RK45dRh/f38DV/15TraL1HGjpkNpOTx8KfzjypQccsO2Gv7w1hreXFFJXaTzDdloNXpjWpW7ufm2FAQdfnD2SO5962M+rmqn9UrDA1M5lJ7qNRSufQf+MhWWPeP1SltwYJ251UViXHzve3y0cd9fD/deeTSfHZ3dcY0zyWr0xrQjl2Jgov4+ZwMA1502PMslOUBdD4HP/MKb3zT/gA8TnvNnzvz0TwSI8bXJQxvGEb7mb81HRs1vFuiNyTMrPt3DL55dyikjyvnSiUPa2Tr+qyUHv80Gn+ANmLP4sfa3bUn1Vrq9+l98M/gEcwuvo8SJsurmxpz/PW+s5qONO1NT1hxnqRtj8szbK6uoj7r0715ETBUnkSC+/DnYvNAba8AJNI5FUNgNirpDYVdvXINovTfQTnzgnvgrVg9DJnvt5lOluCdM+jLMmemV5exb9x9MqC0FpQ2zPWUv00f3QET4+bTR/PSZJcx4eQUzXl7BOWP68ttLJlIQzN96rwV6Y1qRg89JJeRLJw1h86467n9nLeu31XDP5UfRq7Sg5Y27HOJN37o1+ROP/rzXJ30qnfUr74GqD+6DgcfA+Eta3zY+glzAH6qzavk+q8u6e615LjtuED1KQjzw3nrmrd/Bi4s/5Ye76zi0V4oHdckhFuiNacfBlqMPBhx+8rlRjO7fjZueXMS0u2cx88pJjOrfQv/yhx4LN671xlRW1xsDOD5OsBv1nlyt2+lNnZDXV02w0BteMFgIwSJvrOQHpnuD4aT8YgrhmrdgxuGw7Nn9A33dblj6NMyZycbdG9gVrWE0BV7fOgseAnFYNOgKxq5/AI15rW1CAYfpEwYwfcIAfvTkIh56fwOTb3uDRT87KwV9BuUmC/TG5Kn/d/RADuvThWv+No8Lfv8OMy4cz+fG999/w1T0Gx+LpG8owVAxjLkAFj/hDWEYL++OdfDYV6BiLnUinDPkUKAbC9ZuIPDqf0P5kXDhn6lZvAKASLhuv0N/dfIwHnrfu3H906eX8L9fmJCea8iy/E1KGWMYf2gPnrn+JMb07871D8/n1peWp6dLh0gNhNKY+hg5FcJ74LahsGUJ/PM/4LfjoWIu9B1L/Snfa9g0MPm/4IaP4BuzYfT5SNBLW8Wi4f0OO7SslFnfPx2Aip0d6DriIGM1emNacZCm6PfTp2sRf//a8fz0mSX8/s01LNu8m99eMpHuxSlMU0Rq0zs4+PApjfO/P9Gbjr4AinvAGT+he0FXZi3oT2mf0V46qgkJeNcZDe8f6AFqwl5K56w8bldvgd6YduR6XzeJKAg6/OaCsYwZ0I2fPbOE8+95h5lXHs3hh3RN/uCxKMTC+7RySblACI692muBM+4SGHuRN4RhE92P/lKLuzbW6OtbXP/swk0A9CrNz/w8WOrGmE7l8uMG8/evHc+euiif/927vLIkBaM/hfd603SmbgDOneH1j3PBvfsF+bY4fo0+Fmk50G/d6y3/zKj8rdFboDemkzlmSC+evf4khpWXcvXf5nHXa6uSO+DuCm/arYUbvTnA8Ue1ikVb7g1z8646BvYsprTNzt9gZ02YcNRNefkywQK9MZ1Qv+7FPHrNCUwd24//eXUln2yvOfCD7fLHru1+aGoKl2IS9HP0LdTo52/Ywdsrq/jc+P5Is3a02uRBitWVezn51jc4765/s7c+mt4Cp4EFemNaEfVbpwTy9P+SolCAz47x0hX1ydRUq6u8aWnmB0xPRMDP0bst1Oi/8dCHdC0K7ddVxI+fWsTJt77B0wsq+GjjTs7837fYWx9l5Za93Pz8skwUO6XsZqwxrYj/TC8IpOFBoHxS1N2b1u/Jbjla4cQDfQs1ehHh5MPKQOBHTy5iaFkp4ZjLg7O9tvU3PLKgYduLJw3k0bkb+efcT/j158fs9wsgl1mgN6YVDYE+j/tASYmS3t60Zlt2y9EKJ9R6jf7Ift14ftFmnl+0eZ/lBUGH7589kpF9u/Lk/AoO79OF2R971/eVyUMPqiAPFuiNaVU46rWvtkDfjniNvq6VEaOyLOC3unFbGJrwh+eORFWp3FPPpccOYsKhPehVWkDf7kUN25x0mJeS+s2LXt85hcGD7xeeBXpjWhGOeTX6Qgv0bWsYvCQ3P6d4s8p4CqepYeVd+NN/HJPQce657Ch+9NQi7np9FccP68WJw9u/J1EXiVEUyv4XQ27+yxiTAyx1kyDXb4XiZD+gtSQW8Z6IjadwDtTUcf247tThqML8DTupj7Y+JGFdJMbXH5rHuJ+9wvwNO5I6bypYjd6YVoSjLiI0jEpkWqF+wHNyM5y4MT/QB5J/8vXZj7ynaGe8vILXlm3hn9eeSMD/+1i3tZraSIwXF23mgdnr2VnjpYqeXrCJiYN6Jn3uZCT8LyMiAWAuUKGq5/nLrgf+E4gCz6vqjS3stw7YA8SAqKpOSkG5jUm7+qhLQcA56G68ZZzrN81MRzfFKVDYxevtMrxne9LH2lPX2Ib+ww07+e1rq3jgvXWEo25DnzkAJwzrzVGDe3DPG2sY1a8bry3bQk04xtSx/XCyUHHoyFfwDcAyoBuAiJwOTAfGqWq9iPRpY9/TVXXrgRfTmNTYVRvh9pdXNPxPKdI4iF48nguCiPfzvLOkbZLq0TKeutmy2OuTJj46lUjjPAK9hkFp75SUtyO6lXlP7IZ3b0n6WHd8YQKzVm0lEBBue2kFd762imOG9GTsgB70615EYcjhtBF9GNS7hDeWVwJruPHxjxr2f2zeRu698uiM5+0TCvQiMhCYCtwMfMdffB1wi6rWA6hqZVpKaEwKfbhhB3+bvR6AAT2KG55+VBrvKSraMD/58Nx8CChVSvyA87fZ6/jV+WMP7CDxzsz+9dO2t+t/FFz9xoGdIwk9yr1AH9tblfSxJg7qycRBPVFVBvQoRkQ4Z0xfQi08VTeir9dhXFHI4faLxrNgw07um7WWZxdu4qJJmX2KONEa/R3AjUDTru5GAJNF5GagDviuqn7Qwr4KvCIiCtyrqjNbOoGIXA1cDTBo0KAEi2VMx8Rr749fdyJHD85u3jQXTBnp/RBPqov6vmPh2lneA1PqNo5QFZ9H4a0Z3khVWeDEWwOlcGxIEWH6hAFtbjOgRzHrbpna8H7q2H48PGcDH23clXuBXkTOAypVdZ6InNZs357A8cAxwKMiMkx1v0/zJFXd5Kd2XhWR5ar6dvPz+F8AMwEmTZqUL12BmxzTmG+3PzEAxxF6lxaQVNpYxAv2bfngTxCuTuIkB65hwBEnu90QiwgnHVbGk/Mr+OYZh1PetTBj504kAXkSMM2/qfoIMEVEHgQ2Ak+oZw7gAvv9zlXVTf60EngSOLb5NsZkSkOYtzjfwFVNf5/7sQgEstMqJ+oHek1Bq5tk3Xj2SPbWR3no/fUZPW+7gV5Vb1LVgao6BLgEeF1VrwCeAqYAiMgIoADY54ariJSKSNf4PHAWsDiVF2BMR8Qr9BbnGykZGADdjWStRh0New9MSQ4E+gI/l5/pm/zJnO1+YJiILMar6V+lqioi/UXkBX+bQ4BZIrIQmIPXBPOl5IpszIFz/IhmNfpGqo2fS9rEIl6LnCyI+EMItvRkbKYN7FnMsLJSHp+3kdpw6w9cpVqHAr2qvhlvQ6+qYVW9QlXHqOpRqvq6v3yTqp7rz3+squP912hVvTn1l2BM4uLhzLVI3yAjn0VOBPrs1+gdR/jBOSNZU1XNZffN3qfP+7SeNyNnMSZXxFM3FucbaQZSN5EaCKVxTNk2RKOt93WTDWeN7stlxw1i/oadVO1peXjDVLNAbzqV+E1HtSx9AyUDA6CHq6EgzWPKtiLq1+gDOZCjj5vkN+2tzlD6xgK96VQca125H1XNTI2+IFs1+ninZrkT6A/r0wWAV5emYHD2BFigN51KvB19Ug8I5RmvRp9m4eqspW5i9bUAOKHs/KJoydgB3Tn9iHL+55WVPL2gIu3ns0BvOpXG5pUW6eM03Tl6VT91k6VAH/YGPg8UFGfl/C0REW6/aDxDy0q54ZEFnHzr6+yq2X9glFSxQG86FXtgan+KpreHzmg9oBAqanfTdFA/dSM5cjM2rneXQp76xkkM6FHMxh213PX6qrSdKzc7kDYmTeIBzeJ8o7TX6AMFgECkLo0naV2wyPslEa3LThcMTf3Xowt5Z/VWYqo4ArdfNJ53fjCFybe9zn2z1hIICDedc2TKz2s1etOpxAOataNvlPZWN44Dhd2gfnf6ztGGwlJvTNtwTXbO39S/V1VRUhjgtBHlbNldzxMfVqCqlHXx+r3ZUR1Oy3mtRm86lYZwZnG+QUZa3RR29Xq3zIKiLl6gj9VmP9ArcNzQ3vx82mj+OW8jT86voE/XQuZv2AnA988emZbzWqA3nUpj6sYifZxqBlrdFHbNWo0+1xIXTy+o4PXljYOg3Pv2xw3zPUvScx/BAr3pVOLt6LfuDVO5u46oq8Rc9acuUVeJxhqXFYcCHNmv6z43K8NRlyWbdhFzva8L11VcbRywxNV9p8GAcNzQ3jk7WlVGOjUr7Ao1O6B6K8TC0KWvl9LJANcfAUsydL62XD/lMBZ8spO1W6vZsrue8Yf2YOEnOxvW3//OWr46eVjKz2uB3nQq8ZGAbnzso3a2bPTc9SczZkD3hvd//PfHzHh5RYfOe8cXJnD+xLYHqsgWzUQ3xcU9YdXLMGO49/74b8DZv07vOX1uLD54efbHtP3iCUM4bUQNp8zwRtpatWXfdNa/V221QG9Mso44pCu3XzSemnCUgCMEHSHgOP5UGqcBYdnmPcx4eQW1kX0fU99RHaYw6HDfVZNwxAuRIoIjTafe/JZddVz30IfURTLXU2FHZaRGf+ZPYehkCBTCv/8Hdqf/IaEGrj8+cA4EeoB/LWtM20SbPLl356UTmTa+f1rOaYHedCqOI1x49MDEtvWjX6DZ8EuRmEtxQYDJh5e3e4yKnbX7HCsXZSRHf8ho7wUw/29+2/rMaEjdSPZSN6rKrNVb2VUboSYcpSjkeAOyN7lVNG/ddgv0xmRaNOb9XxhsFujDMbdhAIn2ZKob2qRl8osoWAjRzLWpb0zdZC/cLdm0myv/NKeFNY1/H0elcQxjC/TGtCL+szrY7CZeOKoNuf725Horn/gXUUZ/bwSLvBuyGaLqAuBkMXUTiXll+NX5YzhheG+CjuCIlyIMiFAQdOiRphY3YIHemFbF/EC//NPdDQ9YicDWvfUJt6CJ/xjI1Yp9vFwZTS0FC6F2R8ZOF6/RS4JfzuC1pKqNxKgJx4i6rtfCSr2/CVf9Vlb+1Hvf2NKq6br4dHXlXgD6ditieHmXtFxnWyzQG9OKeDD/zqML91s3bmD3/Za1xMnx3jLjxcroLYRAAUQzV6OP5+iRxGr0v3lh2T5t21OpKJSdXxUW6I1pxelHlPPAl4+lPuo2pDjigXFk364JHSPXu1zISuomFs7ssIJ+jT7R1M2iil0M6FHMF08YTElBgFDAwRHBcbwWVfGb8wE//dLY2sqbd0QQfxqfF4GSgiDjBiRWQUg1C/TGtCIYcDhlRPsta9rSOBh5bgb6+C+NjNboI3UQylyXwa6fo0+0eWVdJMaw8lKuOXV4OouVUdl/VMyYPJb7qZv4vYcMRvponZenzxBtaHWTWLirjbgUBnOjzX2qWKA3Jo2cnE/dZOWsZDJZpH6O3kmweWV9JEZxgQV6Y0yCDpahCzOauhGHTHYfqm7HcvS1kRhFOdov0YHKr6sxJsc0Nq/MzUgfL1ba+7rZh4CfN88Edf1zBRLP0VuN3hiTsMYcfY4GehqfD8gYkYzmjFTjqZvEa/TFWWoGmS4W6I1Jo5y/GdtQo8+gTKduYom3ulFV6iIuhRbojTGJyvl29P40s32uZbpGn3gXCPVRb1ur0RtjEiYNOfrslqM18XsHGe0CwY1ktIOxhlY3CeTo491JF4XyKzTaA1PGpFE8gG7bG2bt1mq/T5QmfaS4+45IFXCEUMAhGBBCjjcNBoSCgEMw4PWbHwo4+3WdfKCy8v0TrYeS3i2vi0WhbhdEqqG0D4SKWj+O63p95lRXeU/bhoq9DtNQ7xzROkAorN4EQMXOMNUVu3BVKQg6FAYDFAYdf957Hx97IN9q9BbojUmjgAihgHD/O2u5/521KTuuCIQcL+DHb6hq0+7NlRaXq2qT+cbjNe+KOa3cGKx6BW7uB/j9AyBeocN79922uKf3peBGvf1iEe8XQSzqbavtD+ji94LPD55bw0ZNbNzafGt1Y4HemDRyHOHBrxxHxc7a/fpAaToiVXxdzFUiMSXqut405hJx/Wksvqxxfcx1EWnSOFIam0qK4I9+FV8lTeYbVxQEhKnj0jPgRYvO+Amsn9X4TaNKw1dRUXcvuIeKYW8l7N4Etdu9VI8TgoA/dYJQ2MWr9ZeW+R2l1UGk1hsyMFjkLUOJRqMs2Ao/LD+xoXvgcMylPhojHHWpj7rURxrfI8JpI/pk7vPIAAv0xqTZccNaSVN0Vkec7b0yJAhMytjZclPCdxxEJCAi80XkuSbLrheRFSKyRERu68i+xhhjMqMjNfobgGVANwAROR2YDoxT1XoRaeu3zj77GmOMyZyEavQiMhCYCtzXZPF1wC2qWg+gqpUd2NcYY0yGJJq6uQO4EWjaQcUIYLKIvC8ib4nIMR3Y1xhjTIa0G+hF5DygUlXnNVsVBHoCxwPfAx6VZp1at7FvS+e5WkTmisjcqqqqhC/AGGNM2xKp0Z8ETBORdcAjwBQReRDYCDyhnjl4NfayBPfdj6rOVNVJqjqpvDy5UX2MMcY0ajfQq+pNqjpQVYcAlwCvq+oVwFPAFAARGQEUAFsT3NcYY0yGJNOhw/3AMBFZjFdbv0pVVUT6i8gLqSmeMcaYZEkuDoggIlXA+myXI0FlNPslk0fy9dry9bogf68tX68LUndtg1W1xbx3Tgb6g4mIzFXVvHzwLl+vLV+vC/L32vL1uiAz15ZffXEaY4zZjwV6Y4zJcxbokzcz2wVIo3y9tny9Lsjfa8vX64IMXJvl6I0xJs9Zjd4YY/KcBXpjjMlzFugTICLjReQ9EVkkIs+KSLyr5t4i8oaI7BWRu9vYf4aILBeRj0TkSRHpkbHCtyEF19VLRF4VkVX+tGfmSt+21q7NX3eTiKz2x1L4bCv7TxCR2SKywO+D6djMlb5tyV6bv11CY0lkUiquy9/2uyKiItK8S5asScHfY3IxRP3Biu3V+gv4ADjVn/8y8Et/vhQ4GbgWuLuN/c8Cgv78rcCt2b6mFF3XbcAP/Pkf5Mp1tXNto4CFQCEwFFgDBFrY/xXgHH/+XODNbF9TCq/tdOBfQKH/vk+2rykV1+VveyjwMt4Dl2XZvqYU/pslFUOsRp+YI4C3/flXgf8HoKrVqjoLqGtrZ1V9RVWj/tvZwMB0FbSDkrouvIFn/urP/xU4Pw1lPFAtXhtemR9R1XpVXQusBlqqrSuNA+V0Bzalsawdley1JTSWRBYke10A/4fXLXqutTJJ6tqSjSEW6BOzGJjmz1+EV2s4UF8GXky6RKmR7HUdoqqbAfxpLo2o3Nq1DQA+abLdRn9Zc98CZojIJ8DtwE3pKeYBSfbaEh1LItOSui4RmQZUqOrCdBbyACX7b9ZUh2OIDQ7uE5F/AX1bWPUjvA/2ThH5CfAMED7Ac/wIiAIPHWg5D+Ccab+ubDnAa5MWtm+p9ncd8G1VfVxELgb+BJyZfKkTk+ZrazqWxDF4Y0kMUz8vkE7pui4RKfGPcVbqStsxaf43i5/jgGKIBXqfqrb3P/FZ0NAl89SOHl9ErgLOA87IxP9QcWm+ri0i0k9VN4tIPyCjKYADvLaN7PvLZSAtp2WuwhvrGOCfZHgozDRfW8NYEsAcEYmPJZH2EX/SeF3D8XLcC8Ub/2gg8KGIHKuqnyZb7kSk+d8sqRhiqZsEiD/wuYg4wI+BP3Rw/7OB7wPTVLUm9SU8MMleF17N5Cp//irg6dSVLjltXNszwCUiUigiQ4HDgTktHGITcKo/PwVYld4SJy4F1/YU7YwlkQ3JXJeqLlLVPqo6RL3xLzYCR2UqyLcn2X+zpGNItu9GHwwvvJrdSv91C/4Txf66dcB2YC/eH9cof/l9wCR/fjVeHm6B//pDtq8pRdfVG3gNLwi+BvTK9jUleG0/wmvdsAK/ZU0L13YyMA+vRcT7wNHZvqYUXlsB8CBe3vhDYEq2rykV19XsWOvIrVY3yf6bJRVDrAsEY4zJc5a6McaYPGeB3hhj8pwFemOMyXMW6I0xJs9ZoDfGmDxngd4YY/KcBXpjjMlz/x8ARgo8yG5ajgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  6589.0 people (VAP of 5311.0 ) and  2749.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Irsxqp4xl3btHUnYMfvwKRlDaT3YlXrtpfjVPbNpDj+u3iKsmCf+wdqhP1bf2JvG5fo50hNmogZTw227PcS4k+Ucs1Hb4Yfk0I4NcfBZfFZHx3hMRqRKRH+dvSiMLb2X29U/MYdniWn78m930xBUJ7Bj8ZPIhLJhRxfIl8yiLCBHXH5FJ+K/fdYSemGO2EuDmuukZv/hhNuqkMTf81ouwuuKCiSxfMo9R5RZqaxilTC6hsxer6jHviaq2i8hHB3JREfkfwJ2AAluBPwDG4FS2nQnsBj6vqu3u8fcCXwZiwNdU9ZcDuf5Qw1uZffvprQl/QyyuNO0Lb3Oebet/+2U1OZfaCO5sbpxfnXGu6WzU/tBcf/jt3dddyIIZVVb6wzBKnFyURUREqnyC++wczwtFRKYBXwPmquopEXkCuBWYC6xV1ftF5B7gHuCbIjLXfb0WmAq8KCIXqmqsv3MYqgQ7i2TqNJJu6+/3Hdx1zQVZr5lN8eTazMg/Bqnht2aqMIzSJheh/z3gNyLihcveDHx3EK47WkS6cXYU+4B7gavd138KvAx8E1iCUxK9E6e3xk5gITDs/Cg3za/mqc176I4p5VGnVEdf6G8EUibFExZDH3Z8cKzQisESwwwjv+RSovxnIlIPXINj1r5RVbf194KquldE/g/QgtPL+3lVfV5EJqtqm3tMm4hMck+ZBqz3vUWrO5aCiCwFlgLU1NT0d4pFY8GMKh5denlGoRdW9K9qTAXtJ7vYd+xUwnfQmaX5UC7FA1dmKHU+lISzJYYZRv7J1Zz0Fk7v7TIAEalR1Zb+XNDtibEEOA84BjwpIr+b6ZSQsVALjao+BDwETlvV/syvEPRX0IYV/UuKOooIbhAUComIqO6eOCsbWpOUQ1jxwLJohM8tqE7saIJRUl7/745T3Tz8+ntDJhTWChsaRv7JpdzHHwPfAQ7gOJgFRxZd3M9rXge8p6qH3PdfBfwWcEBEpri7iinAQff4VmC67/xqHLNVSZJrBdgwIRyMOgJNCPO4gsbC9aOII/i9arI3zq/uzYFw3wecfIhHN7SwqqGVm+ZXJ0VJXT1nUiJxL+67TGd38YWzJYYZRv7JJXT2T4A5qlqrqher6kWq2l9FAY75aZGIjBERAa4FtgPPAHe4x9wBrHYfPwPcKiKjROQ8YDawcQDXLyqZyiNkK52waNYEIu4+KyJu4T96n5eXRZLGwBH0Myeelagm29Udp2nvcSKRsA0bCZPTwY5OIiJEgFHlEc6pHJWiKLzjq8ZU9PPTGJySIP7w42LvcgxjuJKLGWoPEB7D2Q9UdYPrLG8AeoD/xDEdnYXTuvXLOArlZvf4Jjdiapt7/F2lHAmVaRWcrcd2x6luvKKyMQX1CgYK3Lqwhtqp47jvmcbE+wmOAtl9uLdzXhx4s/V4knFP3H+8slIi8Mrbh5IyxOecW8nKhtYUhSGQlBTYFwbT12DRVoaRX3JRFruAl0Xk34FOb1BVv9/fi6rqd3BMW346cXYZYcd/l4FHYA0JMoWqZuuxHcQT2jF1VvjtJ7tc85SDCFx14Tms3X4g6TxN/OMIe/ErCqB26ji27j2elCHun9s7Bzp4Zss+VKE86pQ8r29u77OwNl+DYZQOuSiLFvenwv0xBkimVbC/LLj3u7e0R3oE10wVkUSJcoBJlaMoiwjdMSUaASX5dXAUheckryiLcMulNew40JSyw/Hm9g+/egdwHerAoxtbWNnQmij9kavz3nwNhlE65BI6+xeFmMhIxzM1BSvALltcm+ixHRFh8cVTOHKii9opY/nxb3anZF6LL1CsLBqhduo4npRWFEURPn3xFJ59sy2hMLyjvdIc2TKuk5SX9r5Hd0+cVQ2tCVNVLmYlK0JnGKVDphLlD6jq3SLyLCGhqqr6mbzObAThNzUFi+61n+xi2eJalrlNjX7RtD8hhGsmnMmaxjaunzeFBTOqePClnQnTlOCYoNY0ttHtKyHy3JttXPuhSbyw7UDSH7UsKglF4bH32ClWNbQmnnvKLFHaIxoBVaeWlQiHOjr7bFYyX4NhlAaZdhb/5v7+P4WYyEjGv1oHJSKOHwKcSKPGfcdTqrYCiR3Ipt1HmXNuZUr71FfePpTIw/CIxZWJlaMYVR5JclbHgR37O1jZ0Mrhjk5+9daBhDP90Y0tRF3zVjAzfMf+joQie3nHQcqikZS2rH1hKCX7GYbRS6YS5fXuw83AKVWNA4hIFBhVgLmNGJKEfDRCLB4HdftRPNOYSGyB3hLi6VqdemadfcdO8ejGltB6U2NHlXHj/Gpe2XGQvcdOA9ATU/7Xz7emhMaC40iPu9qrq9vZ7Xh1p9bvOkJcHUUWiyu3LJzOtPGj+yXsLRPbMIYuuTi41+Ik0n3gPh8NPI+TSGcMAn7b/d5jpxLNi8AR4h5eclzQHBTmhF7V0Oo4oEMS9X70+nvE45qiSMIURZA4yXkVQSf1TfOr+y3gLTrKMIYuuSiLM1TVUxSo6gciMiaPcxqReLZ7v6IIosCL2w/w4vYDaQsF+lfnTs5jKsFoKOgNofVeKo8Kl0wfT0PLsSTFEpHkvIrBdFJnio4y81Rm7PMx8k0uyuKEiMxX1QYAEVmAUwDQyAPZEtw8Yd4ZMAd5hEUrZUOASATicSfB79oPT+YrV52fUF6Pb2phW9v7xOPh3fsG00l90/xq1P2daxmUkY59PkYhyEVZ3I1T7M+rxzQFuCVvMxqh+ENnvVBZcKKUIji1oPyiP12ZDW917lWLzQXH3+A8jiucUzkqkRDoOdHLIsKtC2u4cQBmpkwEiyR6e6IFM6oKap4qxRW6me+MQpBLnsUmEfkQMAdnEfqWqnbnfWYjiODK8L5P19K073hihQ3wg1fe5YVtyZnYYbuQBTOqWLa4lr/+j2180Nn3qigKPLaxhdqp42g/2ZVQOj0xZer40WmFUC5CNtMxfoHXFVNWbOhN9CtU8l6prtAtudEoBLlUnR0DfB2Yoap/KCKzRWSOqj6X/+mNDIIrw/aTXXz3sxclCddLpo/nRV9uRJkbFRWkvrmd+55tCi0PkisxhWWrG7nzY+cl1ZRKVzAwFyGb7ZjgjsgfJuyP8srnir9UV+iW3GgUglzMUP8K1AOXu89bgScBUxaDRNjKsL65ndseWpfomnffZ+YlciMiIixfMi9UKKzfdSSRhDcQeuLK/93QnDSWrid4LkI22zGewFvZ0MpT9a0puRqFSN4r5RW6JTca+SYXZXG+qt4iIrcBuH2zM5UpMvpI2MrwW09vdXtNOGaZxze1pG2TWt/czqoGp1HRvKnjKPf5PHKhLCrMnHAmOw9+kDQeNGOl84FUjakgIk42SDohm4sg9gTeTfOrQ1fJ+fYn2ArdMNKTi7LoEpHRuLJCRM7HV33WGByCK8OgNt7SepymfY5pyMvg9pzQt/2otypthbsLadp3nJd9SXfZ2H34g4yvV0SFeVPH8eBLO1NCdZc/10QsrkTdcuZhQrYvgjhslVwof4Kt0A0jnFyUxXeAXwDTReQR4Argi/mclAE3zq/msU0tiSglcExDP3h1V6I67LLFtTy+qSVpF9EVUxr3HeevP3sRn//Bb3JSFj1pOux5nH1mBd/4xJykAoeesPbMSwqoasbQ34EI4lL1JxjGcCGjshCRCFAF3Agswlnw/omqHi7A3IykQh+9xNVpgfrnP99KmJx/qr6VeVPHhXafiwhMGXdGWiVSFhWqx49m95GTibErZ09kTWNbwvnsr0+1Zc+x3tkKfe5tkatpqZT9CYYxHBDNkrglIq+q6pUFms+gUVdXp5s3by72NPpFfXM7D7z4Nq+/czjUTyA4NaJ60tTniArMmzaON1uPp5wfEbho2ji2tKY6qwX4+NzJ/Oqtg0nvHY0IqproqRGNwG9/aHJSsUH/++dqJuqraakUcyAKjX1GxkARkXpVrQuO52KGekFEvgE8DpzwBlX16CDOb8SQ6ctc39yeiAYKVov1M3nsKC6qHs9LOw6mNSFta3s/XNGIcMulNTTu25pk4ooAFeURJlaOSikH4n+uQE8cng/kfHj0xUy0qqE1abeyqqE1o6Azf0JmSjVPxCgNclEWX3J/3+UbU2BWfy8qIuOBh4F57nt9CdiBo5BmAruBz6tqu3v8vcCXgRjwNVX9ZX+vXUzSfZnrm9v54Svvsnb7gSSzUgS4qHqcW6K8d/zIiS7Wbj+AADPOHkPz0ZNJ1xk3ppxjJ8PzJq/90CTmnFuZZN0S4IrZTuMjgKc270lEYvWViJCTmai+uZ0nN+/prTkVEZ7cvIcetwy6Cbq+Y5nuRj7JJYP7vDxc9++AX6jq50SkAhgDfAtYq6r3i8g9wD3AN0VkLnArUAtMBV4UkQtVte/pyUUm7MsMcNtD61KEs+Cs9G+5tIbafcfZeaCDzp44k8eewYvbDyRqRAUVBcDxU91EAxVnBUeIf+Wq81m/60iSUlJINFACuLluOo8EChounFlFfcuxlF1HRKBuRhUXTK5knpv1nYsAWb/rSMLUJcDcKWMTfb+HkgO7lISiZbob+SSXDO4zgP8GfAxHrrwG/EBVc4vJTH2/scCVuBFVqtqFE567BLjaPeynwMvAN4ElwGOq2gm8JyI7gYXAuv5cv9D4hU3wy1w1poIHXnw7dBV/6cwqbvhodUoEEsDa7eEmII94HM4+q5xDH/RGJl1cPY7LZ03ggRffpnbKWMp8Pg8huXRI7dRxoa71v1wyj2WrG5P8GQJcNWdSSkHDbAQ/i3R9v4tJqQnFQuWJ2A5mZJKLGepnQAfwD+7z23C66N3cz2vOAg4B/yoiH8HJDv8TYLKqtgGoapuITHKPnwas953f6o4NecKEzSN3LuKHr7zLrsMnWLY6vNkQQEPLMWZPrgxtcLRgRhUbd6dGOnkoJCkKgFFlEX7w6i4AXnvnMFfOnsivdx4mrhCNCnvdKCav811wWht3t3PDR6t5/CuXp82y9u4511yKoGBL1/c7H+Qyz1IM1x1Ome6lpqyHO7koizmq+hHf85dEZMsArzkf+GNV3SAif4djckpHWLZ4qIgVkaXAUoCampoBTHFw8Aubzu44KxuckNagczgsnNVroxrciTz40k7GpanRlIm9x5Kryr/2zmFESBQJfHRDS0IBpHNXrGls4/bLalgwo4qxo8r4RdN+PlV7bsZS4t7n4AnloJAOlvwohDDIVQhZuG44w3EHY2QnF2XxnyKySFXXA4jIZcCvB3DNVqBVVTe4z5/CURYHRGSKu6uYAhz0HT/dd341sI8QVPUh4CFwQmcHMMdBYdGsCZRFhC63vPiTm/fwqzNTTUgVZRHmTh2XpCwi4mhJr8RH1ZgKlj/XxOnu/tV9ajuebDV0kuiSn2crEVI7ZSwAKza0JHYpP3h1FzUTzuT2y2pSvtwrG1pZ1dCaEMrLFteGJvYVmlyFkJX/SM9w2sEYuRHJ4ZjLgN+IyG4R2Y3jK7hKRLaKyJt9vaCq7gf2iMgcd+haYBvwDHCHO3YHsNp9/Axwq4iMEpHzgNnAxr5etxgsmFHFzXXTE1uj7piy//3kSikLZzolxV95+1BiLIITHfToxhaWP9fEolkTaD/Z1W9FURZJ3Zz1tbiXAJWjywFnh+FnTWMb9c3t7Dt2irKIEHUjogSShPKaxrZQB3+h8YSQN89MQmjBjKqE6c8oLJ6y/von5pgJagiQy87iU3m47h8Dj7iRULuAP8CRkU+IyJeBFlyfiKo2icgTOAqlB7hrqEZChdnBb5xfzUpfPkGQhpZjQO+qXnDCZb2EutPdcZY/28Tl/VxVRSPCnR87j5+s2013T5xoNMLVF57DsZNdGf0eQcqjvSXRr583hdfe6U3ir50ytrdxUTTCLQunJ/pwrGxoTawMa6eMZd27RwAlGo0k/CRh5ql8YjuG0sFya4YOWTO4S5VCZ3BnsoN7VWG9PIJ0Tm2PGy6Zys/fCLW09RkvV2PS2DMSEU6vvH2IHjdxY0xFlAlnjkoKwRWcnY2/93ZFWYRH/7D3nlZsaGFNYxvXz5tC+8kuvvf8DsdZLvD1T8zhrmsuSCQZCk6E1fLnmujsjhOJCILzOQwl85RhGAPL4DYy4K2I9x07lWKv9wtKgKvnTOLA+6dDS234ecNXbykXwsJcI+5gHNzrhV/zg84YH3SeJCpO9nVE4C9vuIg551bywItvJyKmYrFk2/7tl9Vw+2U1ic8grB/HFx5eT2d3nGhE+O0PdSYKDsZdbellboeZpwq92zAMIzOmLAZAsG90WTRCLOYIxyc27wktxRGNOKvvTAnS/iJ+2YhIsqPau8a1H5rMf7a0p4TQpuO8c87isx+dliSY777uQjbtPpoSjeWZo/yCPGjWefClnb0tWePK2u0Hej+faARUicWd/hfXz5uSdJ1FsyawYkMLy1Y3Eosro8ptt2EYxSYnZSEi5+IkwimwyXVSj3j8UTWxuPLbH57E6e4Ynd2xtP6AWNzZCZRFhMUXT2H1G/vS1oA6a1Q0ax/tcJOWJGV558J5E89MSazzKwEvGsvzS6CaUpoj2CY14lOKCnxuQTXTxo8OVTb+HAsgKfmvq9vCJouN7fKMXDK47wSWAb/CkXP/ICLLVfXH+Z7cUMffIS4akYyF/fx4q+3GfeHF/jw8RRGRdEohnGBJDo8rZ0/kddes5EeAr151fsrxfgERDDf17qMrQwFA8WmLsmiEm+ZXp+RV+B97zx98aSdx33YpkqbfuFEYLDnOgNx2Fn8KfFRVjwCIyATgN8CIVhbBDnGXTB/fp+giIKWNaRgRYOJZFRzsyM2clI6FM6v4VCCKyeP8SWclPQ865MuiET5SPS6hZPy6Jq7w+OY9xAO7jPW7jiR8E4Kzq8hVwHihrdn6jRuFId/JcbZrKQ1yURatOOU+PDqAPfmZTung7xAXjystIQX9BgWBIzn6HTKx5+hJvvX01tDX3j34Abc9tI5Hl14OkHBMe0qhqyfOpgyK0NtNeVnqC2ZUpSRUeaG0ueCZv7y+4nPOrcz53KFMqQrFfCbH2a6ldMhFWewFNojIapxF5RJgo4h8HUBVv5/H+Q1ZvC9QZ3ecOCQl24VFJ3njkQhJ5cbD8Jud0pmf+mqaans/fdt0xWnHurKhlWnjR6fNCYHe6rWxeDzlPhSnS59nbhpoLsNKN/t7VUNryQuRUhaK+cxLsZIepUMuGdzvAj+nV/6tBtqASvdnRLJgRhVfvHxm6GvpBO2lM6tYUJP9i5At9SUi8Fc3XMTH507O+l59QXD8MP7LL5xZRUXUycquiAq3X1bDfZ+uTXuTXogtDCz7OV0591Kl1O8nX5nsfcmmN4pLLv0s/qIQEyk1Vmxo4aHXdmV0UAd57/CJnEJZs71nXOHf1u1mx4GOLEfmjoiTD9J+siuxM/LKj9/w0epEAt7tl9W4Dujec6NuiQ8vFHYwvvBh5dy9sN1SXHlanaNwLJu+dMilB3cd8G1gBj7loqoX53dqAyOfGdz1ze3c8sN1aXtglyICjCqP8MXLZyaKBAJ89cpZ/GTd7pRKsl94eD1dbjb28iXzQsuL+230QJ8Fgne+P2w3zIRTKr6AoTTPoTQXY2gxkAzuR3AiorbiJASPeNbvOpI2PLWUEJwdhWpvNvWLgcZKL24/kPBhePkOd11zQehqMCjA/QmLiNAT65u93gun/fbTW5N6dfvt2qXkCxgqdY5K6TMzhg65KItDqvpM3mdSQiyaNYHyqPS7T3WxEeDjcyfzkenjE6t2zzyCJNejPd0dS5jF4jg+DehVDH7/hJ8kG31MAQ0V9tmob27ncV+v7mg02YRjDtK+Y5+Z0R9yURbfEZGHgbVAIqRGVVflbVYlQKmpiXGjy/igM4bGlYpypxe3JyD8JqQXmvYn5X9UnlFORE4n6kZ57VezrU79Nvqou7MI66yXjVUNrUmJjldfeE7a6/jf28ws6TH/idEfclEWfwB8CCin1wylwIhVFut3HckpU3socfxUD+AI/GWLa1ME6JY9x3i+aT+Tx56RNP72gY5ETSe/YMm0OvUEtde4qb8+C0hVyhMrRyU9D3OQmpklM+ZUNvpDLsriI6p6Ud5nUkIsmjWBcjfDuNSIK/zTS+/QcuQETW3vUztlLA//+j2f8juelMOh2lvTqeNUNw+8+DbXz5vSW+pENWVF7wnqYPZ1f4TSTfOreWrzHrpjSnlUQpP7gr4AM7NkZ6j4T4zSIRdlsV5E5qrqtrzPpkRYMKOK35l37qD1nCg0rcdOJyKewsp/zDrnLHYfPuGU8Ch3sq937O/gb3+5I3FOeVSIqxKJCMsW1wJOTae9vlLtcVWWrW5kzrmV/RZMC2ZU8ejSy/u0CjYzi2EMPrkoi48Bd4jIezg+CwF0qIfO5pP65nZWl6iiyIUvXXFeSijs8mebko7pjnl1n5SmfceTKtL6XeTxuA54Zd/XVbCZWQxj8ClWW9WSxqtZNFy44ZKpNO49DiJ86YrzEk2N/D6Ipn3JzZPKo04nvfKySKL6rNck6doPT+ZXbx1M7EyKsbIfKmaWUnK0l9JcjcKTi7IYTnJxUDjUkb7OUqkRFfj3rW30xJRoVGjad5wVG1qSHNMPvPh2Uh2oS2dWcc/1H05KuFvl67X9lavO5+o5kxJZ38NV8IQJ12AiYrEd7bkqAAsKMLKRi7L4d3qrP5wBnAfsAGoHcmERiQKbgb2qulhEzgYeB2YCu4HPq2q7e+y9wJeBGPA1Vf3lQK49UEpde559ZjlHT3QDTruJmGtS6okpj2xoAdyCgdHeZDr/PXttX/3NkvxmHyBhltq0++iAfBZDlTDhCsnK4ab51UV1tPdFAVhQgJGNrIUEVfUiVb3Y/T0bp2Pe64Nw7T8Btvue3wOsda+x1n2OiMwFbsVRTp8C/slVNAWnvrmdbz+9lbWBLOdS49jJ7qzHKI5fotsVIH66Y8qqhtakMX+huf4WzatvbufBl3ZS39y3viDFIOwe/WNdPXEa9x6nLFq8Inl9+TtYQT8jG33uwa2qDSJy6UAuKiLVwH8Fvgt83R1eAlztPv4p8DLwTXf8MVXtBN4TkZ04CmvdQObQV+qb27ntR+tLMlw2SFD4Rwiv4xKNgIgkwmr9px3MYIrzdxDMVfCUmhkkXcSV17QprvBm63HKo8KtC2u4cX7uzZ8Gir+mVq5RYRYUYGQjl7aqX/c9jQDzgUMDvO4DwJ+RXOJ8sqq2Aahqm4hMcsenAet9x7W6Y2FzXQosBaipqRngFJNZ5fZWGI4oTvkPAZ7f1rtrunByJTv2d4Sa3V7ecZD65vYUoRLsIBiWABhGqZlB0gnXR+5cxAMvvs3r7xxGcSrxTh0/uqCKwq90/YmR2eYwVIICjKFJLv0sKn0/o3B8GEv6e0ERWQwcVNX6XE8JGQt1G6jqQ6pap6p155xzTn+nGEqp+ykyocCL2w4wpiLZuretrYN0iephpihI7iCoqonyINkImkG8kuRBk9RQMlWF9XhYMKOKu6+7kFHlxTHpBJVu+8muvPShMEYeGXcWrm/gLFX900G85hXAZ0Tkd3Ac5mNF5P8CB0RkirurmAIcdI9vBab7zq8GCp7k4GUSF7J44AXnnAkiOfXqHigKfc4dCfskMiXE1Te388NX3uXA+6e55dKaRIguJK/U05UkLxVTVTaTTj5DVC0h0cgXaZWFiJSpao+IzB/MC6rqvcC97jWuBr6hqr8rIn8L3AHc7/5e7Z7yDLBCRL4PTAVmAxsHc0654GUS//CVd5NMNflk56ETeXvvuVMq2b6/I6krX7pWsNGIcOfHzqOjs4fHN7UQizs1puZNHZdy/IIZVSxbXJsSNlvf3M7nf/ibRAjultattBw5QeXo8oTQ9H4efGlnqEmqlExV6Uw6+VZ45nsw8kWmncVGHP/EGyLyDPAkkJBeeag6ez/whIh8GWgBbnav0yQiTwDbgB7gLlWNDfK1c2bXofyv8vOF1wEPYOfBD/j4hyenVXwRgaX/ZVaSMAenm96y1Y3E4k4pDyBph+D5LPxhswDLn21K6dn90GtOyZGg0Ey3Oh5I97yBNmIaLAqh8Mz3YOSDXKKhzgaOAL9Nb77FoFSdVdWXcaKeUNUjwLVpjvsuTuRU0ahvbue2h9aVbA8LSN45dMec/hJnlEc43Z3quI8r7Dp8go9MH5803n6yi7g65/bEU2s/+YVhZ3ecH77yLq++cyjtNSBVaAZXx0BCKWQzVYUxGI2Y/O81EEWTDzORZV4bhSCTspjkRkI10qskPEpXYvaTlQ2tJa0ogijwytuH+NJvzeSHr+0irLvu2u0HeHH7gSShumjWBCLiFBGE1NpPi2ZNoCwaSTi5124/kBKqCyRVtg02NILeUiOrGlp5cvMeeuKamMdd11yQ1lQVxmA2YhqoCWmwzUSl4scxSp9M0VBR4Cz3p9L32PsZUYSFZJU6PbE4TW3vhyoKT5h7O4SVbuTTghlVLF8yj7KIEIGU2k8LZlTxuQXVic8rrsmfXTQifGLu5MRzwSmBHuYE/sLD61mxoYWumKYklvUliSzp2KhQ3s/ks5UNrXR29y3ZMCx6KyyKqr/0NwGymAyliDYjdzLtLNpUdXnBZjLEuXF+NSs2toQK1lJFRBhdHiUqJIXIfqR6HLdcWsN9zzTS5ZqrnqpvTfSSaD/ZxfIl85Li91dsaEk4tf2RY4rbqdV7f1UmVo5KMsWE9ahYv+tIou82uOVHfMK9rxFHQbOW/zX/3P3+l+D7PVXfW0AyGpGsiqYQq/5Si36ynVDpkklZDMfFdL9ZMKOKJR+ZWrI9LMIQgRe3H3Dansad1XskIkweewZzzq3k5rrprNjQ4iSXxeKsamh1zHG+L/qO/R1886kticit1945zF9/9qKkcwHKIoK6jZJuml/NTfOrM5piqsZUJNk6r5s7ma/6WsFCryPXW6lm65QXPBdgxYYWvvX01sTcgRSFUd/czgMvvk23Lynz5rrpWYVcrs7sgfocbpzv7OQKmSXeX0opos1IJpOyCHU2j2RmT67MftAQY+HMKi6YXEnr0ZO86mt0JJAo4yFx5doPT2bt9gPE4srz2w6w9q2D/OWSeYwqd/wPIsKhjs6kL3q6MOI1jW3cfd2FrPRVog1mEmczQbSf7EqYwiICl0wfn3Moal8E0prGtpTnweiuLzy8PsVBXzkqe2xILqv+gay0g+feGLJDG2qU2k7I6CXt/3hVPVrIiZQCVWMqij2FPlE1poyGlnY27k4VzP5VezTq9KTwm6JiceXf1u3m4mnj2LS7nZgqL+84SDQiaExBJG0YsZdf8cidixK9P/wRU7kIyFyFSrB4n9f2tSzqnBvmPA/O1d8t8Pp5U1LevzMkkqup7f207+mRizN7ICvtUlylWx5I6dLnQoIjmVxLVwwV2k/2ZD1GgJqq0by842DKa9v3dyQ990eDxeLKe0eSkwYF+MqVs5JW5p7Z6qnNe7i5bjo3uuanbEIuV6HiFS1Udcxor79zmA3vHSUedwV8FieTN9d0PougOcwjqFT6y0BW2qW6Src8kNLElEWO1De380qIQC1FLp1ZxZbW4/TEHIHd30zxeBwumHRWohxJRKBydHni9aRVf0xZsaGFlQ2tLFtcm1HI+W34/p4ZQbwEwJ64JhxsXkis9ziWQ1vX2y+rSevY9pvDBJgxYQxLrzw/7fHB+WXbQQ1kpW2rdKOQmLLIgXR261Jl697j3PdppySHVx01G35h7FEeddqwLn+uKVTweytf73PzBHn7ya60Qq6vDXs6fe8NjsIqi0ZAlVg89xLp6fCXW68oi/C9z18CkFPmeK5mooGstG2VbhQKUxY54H3phwtd3Y7Avvu6C9m0+2hSiGqQiDgCqWpMBb/acTDhFI8I3PeZedx+WQ1zzq0MFfxenag/d8uDQG/IabpIpr7Y4RfNmkA0IvS47y3AFRdM5O7rLgQGXtIjrNw65N4qtVTNRIYRhimLHCjlL/mYiigfu2BictSSOCtmz4zxN2u2hzrBwTG/eJFLwVjqNY1tKaU+gCTh2X6yC9VeYe4POV2xoYVlqxuJa292dl8ErJcg6H+Pu6+7MKlsSH/xwmU9ReqVWw861DMps8E0E1lJj2Ts8yg8pixy4IWm/aElK0qBrlicq+dM4tV3DiUEX1zhvmcaE0X+GlqOZXwP/71HxPEZe87kTbuPsmxxbdqS4lv2HEMgke3thXfWN7ezbHVjYlfgCd67rrkgtGptOjLtbPz0Rbj4TWHqzt1TXDv2dyQ+j7hmj5AbDDORJbIlU4jPw5RRKqYsslDf3J6ojlqKaFxp2necG+dX88qOg+w9dhpwHM7Ln22idtq4hMDO6f20NyPb80GsaWxLKh7oNUXyF14Mds1bv+tIwjQFTjb53mOnWLGhJaVqrad40n15swlkryWut1t59A8zCxevrIfiKEfPtOWZybyE9AiFiZArxRDZfJLvz8OUczimLLKwfteRkt1VRAQkIjy2sYW4plZ/3NJ6nK17j6c9f/Y5Z/JOIFJK3X+ivozs6+dNYcN7RxMr8Sc37wG8on0O8bgmma0WzZrgJPx1x0EcpfbYxpZEkcJgraOBfHn9LXG7ehxllmkH4i/rERGSTFvevAvphzDfRzL5/jxMOYdjyiILi2ZNIAKUmns7Alw0bRyN+95P2xoVCFWE/l4W33t+R8oxCiy+eAqzJ1cmVvqN+47zqFcaJK4c7OhMUk5Kr9nKE/Ze0t5jG1uIuQepOs5koTeSaaBf3uAtZtL963cdocfXeEMDnpqB+CH6a9ooxjWHMvkOGTblHI4pixwoNUUBjqnoRFeMeD+2RQKJpkfel0ZEksxVz73ZxuNfmZn4ot40v5pVvvIekypHJTVbgl6zlSfsPbOOf4rRiKQUKQQG9OWdN3UcUTdXojwqoYULPYIl2DUkTyPM7BUmlIMNlwayO+qP72M4m1PyGTJs+SvhmLLIgmd/LzViSp96dwsQjQrqy03wf2mqxlQkOaTjmixEg1+wHfs7iEYcoZsp78FvjhKB3/7QpND5XTn7nETv7r6urJc/10TcNZ3d95l5Gc9PirCKa0oJ9nTXCAplSFYON82vzotpI9POoRjmlOGyk7H8lVRMWWShWO6KGy6Zyr5jp9KGtPYFkaxVL7h0ZhWzJ1eiOLsEvxLwf2m8lqoRkZRIIH/uhD8/4b5P12aMWLppfjVvH+igoeUYz287wPPbDhARZzexbHEt9z3blPA5bG9L7syXDk9o7T12KuFLCfpN0pFrhJVHup4S/jFlYLujdPeYaedQaHPKcN7JGKYssjJv6riiXPfF7Qc40TnwVuNRgfMmnpmxpEdE4I3W42xubk+sgoMrxPrmdhr3HWd+zXjqW44RV2X5c02hgtffiyIed/IT0plu0rWq9fIY1jS2JZUG745lL9+R1EY1GqEsIvTElDjw653JfpN09GVlmU4oB3t2ZCvL3ley7RwKbU4xx3DxyefOruDKQkSmAz8DzsVxBzykqn8nImcDjwMzgd3A51W13T3nXuDLQAz4mqr+slDzLVbxwA8GoCiunD2RX7/rhKbGFN51FUXQhwDOrmPKuDNoO346qfT4r946SCyujCqP8MXLZ/Kj13alOMrTCQR/8b046XMRsrWqjSu0HDmZtDMqj2ZvOuQXWrFYnFsX1tBy9CS/3nk4L4LMy1QP5oaECerB/ALnsnMopDnFHMPFJd87u2LsLHqA/6mqDSJSCdSLyAvAF4G1qnq/iNwD3AN8U0TmArcCtcBU4EURuVBVB77szkJ9czv7jp3K92UGnXcPn0hybCuOojj/nDNpPnqSnpgSifRWavVyLyLilCtfu/1AQjF0dsd56LVdKRFRQvq2pMFeFOkUbjAjHFJNZs1HTybmdu2HUxsghREUWl4i4KbdR/MiyDyzWzA3JN+Ceqg5YofafEYa+d7ZFVxZqGob0OY+7hCR7cA0YAlwtXvYT4GXgW+644+paifwnojsBBYC6/I5Ty+RqxRrQu1tT1VwCuw6fKI3+ziuKbuMSZWjiEYjSeeH+TvKosItbrnxsMifXFeYN86v5rFNexLJeQJc9+HJvPTWwZREQVU43Z3b+iCd0MqXICum+WWoOWKH2nxGEvne2RXVZyEiM4GPAhuAya4iQVXbRMQLi5kGrPed1uqOhb3fUmApQE1N9hLSmfAncpUy40eXc+xUN5CcU1EWdSKVfCkF7H+/M+lcEfjMR6byi6b9iW55135oEl8JrO7Dtr9BwRxmS10wo4q/dCOPPGf4NXMmcc2cSUnFByE8TyMTYULLP5bJtttXu6+ZX4yhQL53dkVTFiJyFrASuFtV3xcJM0o4h4aMhRq6VfUh4CGAurq6AQUylWjSdgqeovAjwDVzJvHyjoN49WbD7lcVftG0P6UlapCwlbVfYGaypXp9IbxigMufa+KROxfxxFcuZ1VDKwc7Ojn4/mnebD2ekqfRXzLNpz92XzO/GEOFfO7siqIsRKQcR1E8oqqr3OEDIjLF3VVMAbxOQ63AdN/p1cC+fM+xWFFQ+SYqjq9hYuUoelxTVASIRCRpJe/R5fafyNSEKLiy7jjVzed/8BtiChVR4ea66RnNNO0nu1JKfNx1zQUpAnywVu6ZzEb9NSmZ+cUY7hQjGkqAfwG2q+r3fS89A9wB3O/+Xu0bXyEi38dxcM8GNuZ7nqXWQjWMM0dFU8JvL5xcyV999iKg19QWEWHxxVN47/AJDn/QmXB4A0Qke/QR9CbNXT5rQlLkVFfMKf0RNNP0xccRTA4MK4XeFzJdz0xKQ5vhkvRXiohmy9Ya7AuKfAx4DdhKbyWNb+H4LZ4AaoAW4GZVPeqe823gSziRVHer6pps16mrq9PNmzf3e54rNrTwrae39vv8ocxXr5zF+5097DzQQX1ze0KwR4REFFNcnV3IX95wUVIL0frmdn74yruJbOo551YmBQJEhZQQ2y9cVsONbjkQxdm1BUuaQ/ZmRYMZGjiYPouBXMvIHUv6KwwiUq+qdcHxYkRDvU64HwLg2jTnfBf4bt4mFUKp7CwmnlXB/Jqq5OZGWfjBq+El1z0l4SERoeXICX7vXzZw/bwpzDm3klsfWpeoJruldSufmDs5KWkuqCiiEUmErq707WSCZqdcVvC5mojCEgrDnOvpBM1gmpRMwA0elvRXXCyDOw3vHOgo9hRy4siJLlrbT+blvXtimlAsr71zmIUzq5LKjgMceP805WWR0J2FALdc6rib/F3nUCfPw6ssWzWmIieBmouJKCic0zVmKhQm4AYPMxEWF1MWaXhjz7FiTyEnVGFbW0fCfOThJbFdM2cS7Se7eG7LPrbvD1eAZ42KMr+mik27j3KqO3248Kbd7UQDjnDPFBU0MXlf6HlTx6V0nasod4R40z4nwunlHQcTiiSTQM0l6igonP2NmYohrE3ADR4WdVZcTFmk4ZLp49l9JD8r9nxw5qgoH5yOJbK1b1vo+AlWNrRyuKOTnYd6K9BG3Lofnlr4oDPGq+8cznoNxdkpHO7oTPgsPH+G/4vrL8LnF97+rnMAy59r6t1tuESj2ftuZ/Jp7Dt2ijJXoXmNmfKVuZ0L+RZw2UqjDxeB6r+nTJF5Rv4wZZGGHWlW4UOVjtOxRH/siEDlqLLQIn1+RfI/n3ijzwpx54EOLphcmZKYF8aO/R0pwtvrOvfgSzsTuw3/3D63oDqtnyETweKBtyycnqie25cKstmuUQghnOt1cimNPhx8JOb3GRqYskjDu4dy7wUxVPCsQzEltJ4TOFnZBzs62bG/g9mTK/usLDbubmfj7nYe29jCXwUipaD3i+3vYe0X3gAPvrSTqjEVVJQ5fSzi7nH+irdBv0OmpEBILR44bfzonJzZudJfgZXLef1tkpRLafTh4CMxv8/QwJRFGtInlJcG6RrkxRVe2HaAF/oQPZXuff58dWpvCe+Lrb7jPOENhCqBqjEVScrA23V4Zcq97O6BOr8HQn8FVrbzgsrE3ySpK8t1cimNPhx8JEPV7zMczX2ZMGURQn1zO509xSn4MW50GcdP9RTl2n0lHtJydNGsCZRFJMn85fkhgoIzXWZ4sJ1rzM0074vzG5wdTLa8jVxzLforsLKdF/xM3jnQ0VvsUdOXdw+753wXTBxschW2+fD7DFTQj0TTmCmLEP5mzfaiXDci0HF66CiKcWPKOH6ydz7eZstTA2HZ2AtmVHFz3XRWbGhJONs/fG4lkCo4q8ZUhAr0YMa2P7oqFyG9Y39H1nDZ/tSH6o/Aynae/zOJRiPUN/d2RhSy5/tkK5g4VOmrsB1quS8j0TRmyiKE7W3vF/yaEXHKc4eZh6rHn8GU8aOpb25PTppjYAUPg8I/iF9RgGOau21hTUIJeMl2wS+eF4Xl+SO27j3OFx5en1SN1lMC6b6wfuGQi4PaLwDCkv6C5/WnPlR/BVa2BEDvM9l77BSPbWxJvBaN5FZqpRQpprAdjGsPVdNYPokUewJDjRUbWugYhHamfeWiaeOYNfHMUF9J3cyzefKrv8WTX/0tLph0VmJ8oIpCpLcx0sKZVXx87mQqyiLhTYlwbOE3zq/mrz97Ed9160t5yXZxhdPdcZY/2wQ4ppArZk9M5H/4v5R3XXMB7Se7Qp2zmdixv4MHX9qZtPr28AuAuNsj3CuaGPZF9r7sYcdkei0feJ/JTfOrqSiLOEEBEWH5knnDdrVa6M94sK/tKfmvf2LOiDBBQRFqQxWK/taGWvKPr7Ol9XgeZjQwPjF3Ml+56nxWNrSyYkNL1uOjEUCdkh09GVqX+jnDTZZr3HecJzbvSZxX7laOnTd1XMIRDb07iqAzPRpxkvWCCXr+L9WKDS05Oa7DoqvCjg9Wps0lgqqQ9aFyZSQ5TYt5ryPpc+4rQ6Y21FBnVNnQ3Gw9v+0Av3rrIHd+7DwqokJ3LLXTHcD4MeXcWjedj9eey/pdR+g41Z02jDaI53T+689exE3zq1MLBrp1ocoDZceDxOKOMvCUT1Boe21IvYZHyxbXZg0P9UdXhZkO+uNTKFR9qL5QCv6GwaKY9zqSPufBYmhKxiJyweTKol5fcLrYhZmCeuLKw6+/x+9cNCXt+cdPdvOTdbsBZ7v98Ovv5aQohNQt+avvHGLr3uMsf66JH7zyLl2uguqKKQ0B/0kY/oindOG1qprRieuZDLz/qEK4Ld9WioaRX2xnEaCYTY8iAlPGnUHb8dMJk4v33BPMPXFl9ZZ9KbuKsWeU8f7pnkSI6cqGVvYcPZnUyzqsnzbAxMoKpo0bzS2X1qR18h58/3TSOW/lUGgxm78gF+egt2NY2dDKk55pLODYGYlhjIZRaExZBHh5x8HsB+WBMyuinO6JJTUeiitcNWcSJzt7+Pkbvc0BwwT+B529kUsxhcc2tqSu/NPsBA53dHG4o4vGfY2A0+o0KNBvubSGxn29fbFVnW1pWNnBqMCtbkmR/hYEDB6/fteRRL5FLNZrhqpvbueBF98ecWGMhlFoTFkE6EtfiMHkRFd4BNbjm1pClUOQoGIIPs8lzDYWV5atdhRG+8muFH9Dy5ETyb0wfG8qwMXV46idNi5Rk6kvZDMjhe1GwpzfIyWM0TAKjSmLIU4sfcXwPpHOBBWkx1UYYVFKlaPLE6GwQrJCikaEZZ8Od1SHNSPqa8+JsN2IvxhhhN6KtrarMIzBx5TFIFA9/gz2v3+ankES7PkgFyd377G9CW2rGloTArpqTAURcbYTZREBkUSXvMUXT8kY+upXBP3tORGMYAnuNkxRGEb+MGXh4/7/6HuZDwGunDMpKfO2lPGqxMZiTvmJJzfvoSeulEUjoJoId73vM/OSzFI/f2MfC8+bkFKFNixbNijk+9tzIh81gwzDCKdklIWIfAr4OyAKPKyq9w/2NX70Wnhv6kx8fO5kbppfzROb9hDPMcHRK+0xpiLKG3uOccn08TyzZV+qn0EcZ3EhdyyC01Ni2vjRifITnqAHksJdmwJlUdY0tqUoizBfQ5iQ72/PCYuXN4zCUBLKQkSiwIPAx4FWYJOIPKOq2wbzOjkmOicxsXIUC2ZUsXzJPJatbkwKVU2HAB+ZPj5RcfXBl3aG+hO+8l9m8fHac1n+bFNes8ojApGIoG6DIs9BXd/czqqG1kSRO29n4Qn9qjEVvObrsHf9vNT8j3Sr/6CQN6FvGEObklAWwEJgp6ruAhCRx4AlwKAqi75SEZVEQ5/bL3OynLN1nwtLfls0awLl0eSy3oLjUF4wo4raaeNClcWVsyfSuO99jp7oTWqrHn8GU8eP5uiJLt47cjKpX3ZFVKiZcCY7D36QuMbF1eNY9ulagFCBHiz77T/GO25NYxvXz5uSsqvwMEVgGKVPqSiLacAe3/NW4LLgQSKyFFgKUFMTLrgGwlevnEXl6PKUZj0eC2ZUsfTK8/nW01tTzo3gZGbfXDc9Jf9gwYwqHl16OT945V1+9dZB1I1E8gT0TfOreWrzHrpjSiTiJA56/a/rm9u51VeG4+9um59UVsOr8Bqs6ZSooeSLYMrFqRw85vbLatIqCcMwhg8lUUhQRG4GPqmqd7rPfw9YqKp/nO6c/hQSnHnPv6eMlUVg5oQz+dLHZuUsFFdsaGFNYxu1U8ZmVC5hpMs3GMyid1YawzCMdKQrJFgqyuJy4D5V/aT7/F4AVf3f6c7pb9VZv8L468+m9pg2DMMYzpR61dlNwGwROQ/YC9wK3J6PC+2+/7/m420NwzBKmpJQFqraIyL/HfglTujsj1W1qcjTMgzDGDGUhLIAUNX/AP6j2PMwDMMYiVg/C8MwDCMrpiwMwzCMrJiyMAzDMLJiysIwDMPISknkWfQHETkENPfz9InA4axHDV1Kff5Q+vdQ6vOH0r+HUp8/FOceZqjqOcHBYassBoKIbA5LSikVSn3+UPr3UOrzh9K/h1KfPwytezAzlGEYhpEVUxaGYRhGVkxZhPNQsScwQEp9/lD691Dq84fSv4dSnz8MoXswn4VhGIaRFdtZGIZhGFkxZWEYhmFkxZSFDxH5lIjsEJGdInJPseeTCRHZLSJbReQNEdnsjp0tIi+IyDvu7yrf8fe697VDRD5ZhPn+WEQOikijb6zP8xWRBe597xSRvxcRKfI93Ccie92/wxsi8jtD9R5EZLqIvCQi20WkSUT+xB0vib9DhvmX0t/gDBHZKCJb3Hv4C3d86P8NVNV+HL9NFHgXmAVUAFuAucWeV4b57gYmBsb+P+Ae9/E9wN+4j+e69zMKOM+9z2iB53slMB9oHMh8gY3A5TgtxNcA1xf5Hu4DvhFy7JC7B2AKMN99XAm87c6zJP4OGeZfSn8DAc5yH5cDG4BFpfA3sJ1FLwuBnaq6S1W7gMeAJUWeU19ZAvzUffxT4Abf+GOq2qmq7wE7ce63YKjqq8DRwHCf5isiU4CxqrpOnW/Lz3zn5J0095COIXcPqtqmqg3u4w5gO05/+5L4O2SYfzqG1PwB1OED92m5+6OUwN/AlEUv04A9vuetZP6PWGwUeF5E6kVkqTs2WVXbwPliAZPc8aF6b32d7zT3cXC82Px3EXnTNVN55oMhfQ8iMhP4KM7KtuT+DoH5Qwn9DUQkKiJvAAeBF1S1JP4Gpix6CbP3DeW44itUdT5wPXCXiFyZ4dhSu7d08x2K9/HPwPnAJUAb8D13fMjeg4icBawE7lbV9zMdGjJW9HsImX9J/Q1UNaaqlwDVOLuEeRkOHzL3YMqil1Zguu95NbCvSHPJiqruc38fBJ7GMSsdcLenuL8PuocP1Xvr63xb3cfB8aKhqgfcL38c+BG95r0heQ8iUo4jaB9R1VXucMn8HcLmX2p/Aw9VPQa8DHyKEvgbmLLoZRMwW0TOE5EK4FbgmSLPKRQROVNEKr3HwCeARpz53uEedgew2n38DHCriIwSkfOA2TjOsWLTp/m62/MOEVnkRn78vu+couB9wV0+i/N3gCF4D+71/gXYrqrf971UEn+HdPMvsb/BOSIy3n08GrgOeItS+BsUIgKgVH6A38GJsHgX+Hax55NhnrNwIiS2AE3eXIEJwFrgHff32b5zvu3e1w4KGEHku/6jOCaCbpxV0Zf7M1+gDkcYvAv8I24VgiLew78BW4E3cb7YU4bqPQAfwzFVvAm84f78Tqn8HTLMv5T+BhcD/+nOtRFY5o4P+b+BlfswDMMwsmJmKMMwDCMrpiwMwzCMrJiyMAzDMLJiysIwDMPIiikLwzAMIyumLIyCICIxtyLoFhFpEJHfcsenishTac55WUQK3qxeRK725jcI7/WbPh7/RRGZ6nu+W0Qmhhz3GSlyZeSBzCF4n8bQp6zYEzBGDKfUKXGAW2b5fwNXqZOJ/rliTiyEq4EPgD4J+jBUta9K54s4sfMZs3FV9RkGOWlURKKqGsv1+AHO4YvkcJ/G0MF2FkYxGAu0g1MQTtz+ECIyWkQecwvCPQ6M9k4QkU+IyDp3V/KkWx/IW3n/hTu+VUQ+FLyYiGwQkVrf85fdXgBni8jP3eutF5GL3QJ1XwX+h7sT+i9u1u1KEdnk/lzhvs9V0ttD4T+9rPrAtT9wf1/tXvcpEXlLRB5xM2/9x34OJ9HqEfc9vfv/4+D9uSvzf3Qf3ywije6u7dWQOVwtIq+KyNMisk1EfiAiEW9+IrJcRDYAl4vI74rTb+ENEfmhiETd4z7lzmGLiKwNmcNPxOmp8BsR2eXei3f9P3PnvkVE7s9wn8ZQphBZi/ZjP0AMJ+P2LeA4sMAdn4nbHwL4OvBj9/HFQA+OUJkIvAqc6b72TXozX3cDf+w+/m/AwyHX/h/AX7iPpwBvu4//AfiO+/i3gTfcx/fh648ArAA+5j6uwSk3AfAsTkFHgLOAspBrf+D+vtq972qcRdo67z0Dx78M1Pmeh94fzsr8H93HW4Fp7uPxIe95NXAaJ/M/CrwAfM59TYHPu48/7N5Tufv8n3DKSJyDU/n0PHf87JA5/AR40r23uTjl/sEpdPkbYEzg3KT7tJ+h/2M7C6NQnFLVS1T1QziF034WXFnjNBf6vwCq+iZOSQRwmsPMBX4tTmnnO4AZvvO8gnj1OMonyBPAze7jz+MINXDKR/ybe71fARNEZFzI+dcB/+he+xlgrLuL+DXwfRH5Go6Q7sn0AeDU9GlVp+DdG2nmGka2+/s18BMR+UMcZZDu2rvUMTM9inPv4Cjxle7ja4EFwCb3Xq/FUTCLgFfV6aeAqqbr6fFzVY2r6jZgsjt2HfCvqnoyy7nGEMd8FkbBUdV1rtP2nLCXQ8YEp+7/bWnestP9HSPk/7Sq7hWRIyJyMXAL8BXf++Zy/QhwuaqeCozfLyL/jlOfaL2IXKeqb6WZo3+eaeea5bx09/dVEbkM+K/AGyJyiaoeCR6W5vlp7fVTCPBTVb3Xf6CIfCbk/Ezz9N7L+201hYYBtrMwCo5rd48CQYH2KvAF95h5OKYogPXAFSJygfvaGBG5sI+XfQz4M2Ccqm4Nud7VwGF1+iN04LTt9Hge+O+++V/i/j5fVbeq6t8Am4EUf0k/CF47K+48NqjqMuAwySWtPRaKU1E5gqMwXw85Zi3wORGZ5L7v2SIyA8dkdpU4VU8RkbP7ML3ngS+JyJjAuX2+T6O4mLIwCsVozxkMPA7coamRN/8MnCUib+II9o0AqnoIxz7+qPvaevoumJ/CKTv/hG/sPqDOfc/76S0R/SzwWc/BDXzNO05EtuE4wAHu9hzLwCmcPsgD5SfAD/ro+P1b14HciKMAt4Qcsw7nHhuB93B6oCThmo/+F04HxjdxfBtT3M9/KbDKvdfHc70ZVf0Fjulus/u3/4b70k/o+30aRcSqzhrGMMfdNX1DVRcXeSpGCWM7C8MwDCMrtrMwDMMwsmI7C8MwDCMrpiwMwzCMrJiyMAzDMLJiysIwDMPIiikLwzAMIyv/D5B45y7rnx3cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.5855513307984791 263\n",
      "100 0.6064516129032258 465\n",
      "200 0.4573170731707317 492\n",
      "300 0.5734104046242775 865\n",
      "400 0.645 400\n",
      "500 0.6108108108108108 185\n",
      "600 0.6033519553072626 537\n",
      "700 0.4405594405594406 286\n",
      "800 0.0 0\n",
      "900 0.3933933933933934 333\n",
      "1000 0.09001022843504944 2933\n",
      "1100 0.11818181818181818 220\n",
      "1200 0.07738095238095238 840\n",
      "1300 0.36330935251798563 556\n",
      "1400 0.6546546546546547 333\n",
      "1500 0.0 0\n",
      "1600 0.41402714932126694 442\n",
      "1700 0.40250260688216893 959\n",
      "1800 0.7472118959107806 538\n",
      "1900 0.7174887892376681 446\n",
      "2000 0.513986013986014 286\n",
      "2100 0.6246786632390745 389\n",
      "2200 0.5787234042553191 235\n",
      "2300 0.6388206388206388 407\n",
      "2400 0.5555555555555556 243\n",
      "2500 0.45365853658536587 615\n",
      "2600 0.7023809523809523 84\n",
      "2700 0.4344978165938865 916\n",
      "2800 0.6515151515151515 66\n",
      "2900 0.7541766109785203 419\n",
      "3000 0.5856164383561644 584\n",
      "3100 0.5778781038374717 443\n",
      "3200 0.7301927194860813 467\n",
      "3300 0.19710144927536233 345\n",
      "3400 0.5574127906976745 1376\n",
      "3500 0.36308805790108567 829\n",
      "3600 0.1632010081915564 1587\n",
      "3700 0.29521276595744683 376\n",
      "3800 0.5044943820224719 890\n",
      "3900 0.0 0\n",
      "4000 0.7242524916943521 301\n",
      "4100 0.2797074954296161 1641\n",
      "4200 0.5012406947890818 806\n",
      "4300 0.0 0\n",
      "4400 0.6176470588235294 170\n",
      "4500 0.6968174204355109 597\n",
      "4600 0.4580801944106926 823\n",
      "4700 0.5555555555555556 405\n",
      "4800 0.09722222222222222 360\n",
      "4900 0.46733668341708545 199\n",
      "5000 0.41890639481000924 1079\n",
      "5100 0.6842105263157895 190\n",
      "5200 0.5809716599190283 494\n",
      "5300 0.0 0\n",
      "5400 0.5836298932384342 281\n",
      "5500 0.6148148148148148 135\n",
      "5600 0.5580736543909348 706\n",
      "5700 0.5760233918128655 342\n",
      "5800 0.0 0\n",
      "5900 0.5539112050739958 473\n",
      "6000 0.7241379310344828 87\n",
      "6100 0.6666666666666666 3\n",
      "6200 0.0 0\n",
      "6300 0.0 0\n",
      "6400 0.7189922480620154 516\n",
      "6500 0.586687306501548 646\n",
      "6600 0.6061269146608315 457\n",
      "6700 0.6736842105263158 285\n",
      "6800 0.0 0\n",
      "6900 0.0 0\n",
      "7000 0.6366782006920415 289\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "plotPoly(wholeMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 16.92436274479044 16.323589529374473\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "plotPoly(tractMAP)\n",
    "plotPoly(origMAP)\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  5893715 3240940 0.4968093673346601\n",
      "compare to Census 5893718 Leip has Trump + Biden = 3241050.0 0.4968093673346601\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 5893718\n",
    "atlasTrump = 1610184.\n",
    "atlasBiden = 1630866\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 5893715 5893712.0\n",
      "state Trumpers before, after slice opn are 1610109 1610109.0\n",
      "state Bideners before, after slice opn are 1630831 1630831.0\n",
      "missed pop for tract, pop, (x,y) 346 3 -87.88153308058659 43.04961650406855\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 42 grids for 8 districts\n",
      "starting grid no 0 at x = -92.50383728571428, y = 42.85842975 \n",
      "starting grid no 5 at x = -92.50383728571428, y = 46.523597249999995 \n",
      "starting grid no 10 at x = -91.73264585714286, y = 45.79056374999999 \n",
      "starting grid no 15 at x = -90.96145442857143, y = 45.05753025 \n",
      "starting grid no 20 at x = -90.190263, y = 44.32449675 \n",
      "starting grid no 25 at x = -89.41907157142856, y = 43.59146325 \n",
      "starting grid no 30 at x = -88.64788014285712, y = 42.85842975 \n",
      "starting grid no 35 at x = -88.64788014285712, y = 46.523597249999995 \n",
      "starting grid no 40 at x = -87.8766887142857, y = 45.79056374999999 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 281774.30705897283 2784511.040978819 37.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 8   #WI congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0732781481706251e-08 1.942518699998943e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 0 6.0 3 90.0 3.5741 192320.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 0 7.0 3 90.0 3.4594 192320.3\n",
      "I am working on tract number 20 of 1542 tracts\n",
      "I am working on tract number 40 of 1542 tracts\n",
      "I am working on tract number 60 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 69\n",
      "we have 2 non-opposing shorted wedges for tract no 76\n",
      "we have 2 non-opposing shorted wedges for tract no 79\n",
      "I am working on tract number 80 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 80\n",
      "we have 2 non-opposing shorted wedges for tract no 81\n",
      "we have 2 non-opposing shorted wedges for tract no 82\n",
      "I am working on tract number 100 of 1542 tracts\n",
      "I am working on tract number 120 of 1542 tracts\n",
      "I am working on tract number 140 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 151 5.0 3 90.0 0.6406 320986.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 159 11.0 0 90.0 2.6989 204662.3\n",
      "I am working on tract number 160 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 161 5.0 3 90.0 0.6421 329824.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 167 0 205112.11378794504 1.0218\n",
      "8 yoyos for tract,wedge,wedgePop,r= 167 0 121735.65000653341 0.5109\n",
      "9 yoyos for tract,wedge,wedgePop,r= 167 0 204917.04950601934 1.0117\n",
      "10 yoyos for tract,wedge,wedgePop,r= 167 0 131510.30237976916 0.7613\n",
      "10 yoyos for tract,wedge,wedgePop,r= 167 0 159969.0640044856 0.8865\n",
      "11 yoyos for tract,wedge,wedgePop,r= 167 0 197829.7852562824 0.9491\n",
      "12 yoyos for tract,wedge,wedgePop,r= 167 0 178264.95343950903 0.9178\n",
      "13 yoyos for tract,wedge,wedgePop,r= 167 0 188938.52535046425 0.9335\n",
      "I am working on tract number 180 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 187 4.0 1 51.1 0.955 401311.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 187 9.0 1 51.1 1.0337 401311.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 187 10.0 1 51.1 0.9441 401311.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 188 4.0 0 90.0 1.0027 188500.6\n",
      "we have 2 non-opposing shorted wedges for tract no 193\n",
      "we have 2 non-opposing shorted wedges for tract no 194\n",
      "I am working on tract number 200 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 204 1 211935.5811050929 1.1232\n",
      "8 yoyos for tract,wedge,wedgePop,r= 204 1 124370.30228977338 0.5616\n",
      "9 yoyos for tract,wedge,wedgePop,r= 204 1 212391.76777972985 1.1376\n",
      "10 yoyos for tract,wedge,wedgePop,r= 204 1 147089.04894714316 0.8496\n",
      "11 yoyos for tract,wedge,wedgePop,r= 204 1 208587.64056816077 0.9936\n",
      "we have 2 non-opposing shorted wedges for tract no 208\n",
      "we have 2 non-opposing shorted wedges for tract no 209\n",
      "we have 2 non-opposing shorted wedges for tract no 210\n",
      "I am working on tract number 220 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 220\n",
      "we have 2 non-opposing shorted wedges for tract no 222\n",
      "we have 2 non-opposing shorted wedges for tract no 223\n",
      "I am working on tract number 240 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 242 1 968901.5294443734 1.8962\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 244 6.0 3 78.6 4.1326 365658.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 244 11.0 3 78.6 4.591 365658.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 244 12.0 3 78.6 4.4226 365658.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 244 13.0 3 78.6 4.2603 365658.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 244 14.0 3 78.6 4.104 365658.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 244 19.0 3 78.6 4.4047 365658.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 244 20.0 3 78.6 4.2431 365658.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 244 21.0 3 78.6 4.0874 365658.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 244 3 363413.7031781053 3.9458\n",
      "8 yoyos for tract,wedge,wedgePop,r= 244 3 207219.68270106666 1.9729\n",
      "9 yoyos for tract,wedge,wedgePop,r= 244 3 365658.16576507414 4.2472\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 246 9.0 1 36.0 2.07 848093.9\n",
      "I am working on tract number 260 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 262 1 556348.8845367717 1.2984\n",
      "8 yoyos for tract,wedge,wedgePop,r= 262 1 36957.39724690968 0.6492\n",
      "9 yoyos for tract,wedge,wedgePop,r= 262 1 555904.6850965233 1.2963\n",
      "9 yoyos for tract,wedge,wedgePop,r= 262 1 445847.32682677766 0.9728\n",
      "10 yoyos for tract,wedge,wedgePop,r= 262 1 104972.21440330223 0.811\n",
      "11 yoyos for tract,wedge,wedgePop,r= 262 1 305390.1713590193 0.8919\n",
      "12 yoyos for tract,wedge,wedgePop,r= 262 1 158003.06426494778 0.8514\n",
      "13 yoyos for tract,wedge,wedgePop,r= 262 1 210781.2872173393 0.8717\n",
      "we have 2 non-opposing shorted wedges for tract no 264\n",
      "we have 2 non-opposing shorted wedges for tract no 266\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 275 2.0 0 90.0 1.0038 297246.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 275 5.0 0 90.0 0.9923 306432.9\n",
      "I am working on tract number 280 of 1542 tracts\n",
      "I am working on tract number 300 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 305 2.0 0 90.0 1.209 208117.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 305 3.0 0 90.0 1.1015 208117.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 311 2.0 2 90.0 1.1321 204637.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 311 3.0 2 90.0 1.045 204637.9\n",
      "we have 2 non-opposing shorted wedges for tract no 318\n",
      "I am working on tract number 320 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 323\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 326 5.0 1 90.0 0.5876 445331.0\n",
      "we have 2 non-opposing shorted wedges for tract no 327\n",
      "we have 2 non-opposing shorted wedges for tract no 338\n",
      "I am working on tract number 340 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 351 1 205306.99318775386 0.9537\n",
      "8 yoyos for tract,wedge,wedgePop,r= 351 1 107158.95476058508 0.4769\n",
      "9 yoyos for tract,wedge,wedgePop,r= 351 1 205271.65914641757 0.9527\n",
      "10 yoyos for tract,wedge,wedgePop,r= 351 1 134283.0429855234 0.7148\n",
      "7 yoyos for tract,wedge,wedgePop,r= 352 3 196964.94903703177 0.9195\n",
      "8 yoyos for tract,wedge,wedgePop,r= 352 3 104898.07809980286 0.4597\n",
      "9 yoyos for tract,wedge,wedgePop,r= 352 3 196556.962556081 0.9023\n",
      "10 yoyos for tract,wedge,wedgePop,r= 352 3 128713.71219194704 0.681\n",
      "10 yoyos for tract,wedge,wedgePop,r= 352 3 172732.50318937277 0.7917\n",
      "11 yoyos for tract,wedge,wedgePop,r= 352 3 195255.20825118627 0.847\n",
      "11 yoyos for tract,wedge,wedgePop,r= 352 3 190585.35446626923 0.8193\n",
      "I am working on tract number 360 of 1542 tracts\n",
      "I am working on tract number 380 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 384\n",
      "we have 2 non-opposing shorted wedges for tract no 385\n",
      "I am working on tract number 400 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 418\n",
      "I am working on tract number 420 of 1542 tracts\n",
      "I am working on tract number 440 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 447 1 528801.7883128052 1.0755\n",
      "I am working on tract number 460 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 476\n",
      "I am working on tract number 480 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 494\n",
      "I am working on tract number 500 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 506\n",
      "we have 2 non-opposing shorted wedges for tract no 507\n",
      "we have 2 non-opposing shorted wedges for tract no 509\n",
      "we have 2 non-opposing shorted wedges for tract no 511\n",
      "we have 2 non-opposing shorted wedges for tract no 512\n",
      "we have 2 non-opposing shorted wedges for tract no 515\n",
      "I am working on tract number 520 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 522\n",
      "we have 2 non-opposing shorted wedges for tract no 525\n",
      "we have 2 non-opposing shorted wedges for tract no 527\n",
      "I am working on tract number 540 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 540 2.0 0 90.0 0.6739 270304.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 541 3.0 1 90.0 0.9031 368475.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 541 4.0 1 90.0 0.8278 368475.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 541 5.0 1 90.0 0.7589 368475.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 541 6.0 1 90.0 0.6957 368475.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 541 7.0 1 90.0 0.6377 368475.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 541 8.0 1 90.0 0.5846 368475.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 555 4.0 1 36.0 1.4883 410631.2\n",
      "I am working on tract number 560 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 570\n",
      "we have 2 non-opposing shorted wedges for tract no 574\n",
      "I am working on tract number 580 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 584 4.0 1 36.0 1.5258 427724.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 584 5.0 1 36.0 1.3518 427724.5\n",
      "I am working on tract number 600 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 613 4.0 1 36.0 1.5921 412093.3\n",
      "I am working on tract number 620 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 634 3.0 1 36.0 1.3535 387926.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 634 4.0 1 36.0 1.2943 387926.5\n",
      "I am working on tract number 640 of 1542 tracts\n",
      "I am working on tract number 660 of 1542 tracts\n",
      "I am working on tract number 680 of 1542 tracts\n",
      "I am working on tract number 700 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 712\n",
      "we have 2 non-opposing shorted wedges for tract no 713\n",
      "I am working on tract number 720 of 1542 tracts\n",
      "I am working on tract number 740 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 741 0 203097.84100863227 0.8619\n",
      "I am working on tract number 760 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 779\n",
      "I am working on tract number 780 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 788\n",
      "we have 2 non-opposing shorted wedges for tract no 790\n",
      "we have 2 non-opposing shorted wedges for tract no 791\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 795 9.0 3 90.0 2.2116 434333.3\n",
      "I am working on tract number 800 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 807 10.0 1 90.0 5.0119 299867.9\n",
      "we have 2 non-opposing shorted wedges for tract no 811\n",
      "we have 2 non-opposing shorted wedges for tract no 812\n",
      "we have 2 non-opposing shorted wedges for tract no 813\n",
      "we have 2 non-opposing shorted wedges for tract no 815\n",
      "we have 2 non-opposing shorted wedges for tract no 816\n",
      "I am working on tract number 820 of 1542 tracts\n",
      "I am working on tract number 840 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 841 3.0 1 36.0 1.335 399225.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 841 4.0 1 36.0 1.2363 399225.2\n",
      "we have 2 non-opposing shorted wedges for tract no 844\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 854 6.0 1 56.7 0.8531 368213.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 854 7.0 1 56.7 0.8238 368213.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 854 8.0 1 56.7 0.7955 368213.6\n",
      "we have 2 non-opposing shorted wedges for tract no 857\n",
      "we have 2 non-opposing shorted wedges for tract no 858\n",
      "we have 2 non-opposing shorted wedges for tract no 859\n",
      "I am working on tract number 860 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 862\n",
      "we have 2 non-opposing shorted wedges for tract no 863\n",
      "we have 2 non-opposing shorted wedges for tract no 864\n",
      "we have 2 non-opposing shorted wedges for tract no 865\n",
      "we have 2 non-opposing shorted wedges for tract no 866\n",
      "we have 2 non-opposing shorted wedges for tract no 869\n",
      "I am working on tract number 880 of 1542 tracts\n",
      "I am working on tract number 900 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 908\n",
      "I am working on tract number 920 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 920 0 234487.55392247025 1.0689\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 921 2.0 1 90.0 1.0423 216387.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 921 3.0 1 90.0 0.9212 216387.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 921 4.0 1 90.0 0.8142 216387.6\n",
      "I am working on tract number 940 of 1542 tracts\n",
      "I am working on tract number 960 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 972 8.0 1 57.9 0.9152 455407.0\n",
      "I am working on tract number 980 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 980\n",
      "7 yoyos for tract,wedge,wedgePop,r= 985 1 482043.41773103527 2.0685\n",
      "8 yoyos for tract,wedge,wedgePop,r= 985 1 79444.08548651915 1.0342\n",
      "9 yoyos for tract,wedge,wedgePop,r= 985 1 579214.4543836432 2.1556\n",
      "10 yoyos for tract,wedge,wedgePop,r= 985 1 134714.0854672722 1.5949\n",
      "10 yoyos for tract,wedge,wedgePop,r= 985 1 198543.36608927711 1.8753\n",
      "10 yoyos for tract,wedge,wedgePop,r= 985 1 312812.98211812804 2.0154\n",
      "11 yoyos for tract,wedge,wedgePop,r= 985 1 505587.18866381457 2.0855\n",
      "11 yoyos for tract,wedge,wedgePop,r= 985 1 405931.8465959301 2.0505\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 986 7.0 0 90.0 3.0267 195852.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 992 1 887086.904661851 3.0927\n",
      "8 yoyos for tract,wedge,wedgePop,r= 992 1 171409.3193374204 1.5463\n",
      "9 yoyos for tract,wedge,wedgePop,r= 992 1 878154.5519475443 3.0721\n",
      "10 yoyos for tract,wedge,wedgePop,r= 992 1 294630.3050509689 2.3092\n",
      "11 yoyos for tract,wedge,wedgePop,r= 992 1 760829.8120984619 2.6907\n",
      "12 yoyos for tract,wedge,wedgePop,r= 992 1 408146.596091092 2.4999\n",
      "13 yoyos for tract,wedge,wedgePop,r= 992 1 637266.4373304942 2.5953\n",
      "14 yoyos for tract,wedge,wedgePop,r= 992 1 496499.56218077877 2.5476\n",
      "15 yoyos for tract,wedge,wedgePop,r= 992 1 553602.0521619826 2.5715\n",
      "16 yoyos for tract,wedge,wedgePop,r= 992 1 524141.79372630036 2.5595\n",
      "I am working on tract number 1000 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1018\n",
      "I am working on tract number 1020 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1023\n",
      "I am working on tract number 1040 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1049\n",
      "we have 2 non-opposing shorted wedges for tract no 1051\n",
      "I am working on tract number 1060 of 1542 tracts\n",
      "I am working on tract number 1080 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1085\n",
      "we have 2 non-opposing shorted wedges for tract no 1092\n",
      "I am working on tract number 1100 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1105\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1113 2.0 2 90.0 1.2584 209605.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1113 3.0 2 90.0 1.1402 209605.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1113 4.0 2 90.0 1.0331 209605.1\n",
      "I am working on tract number 1120 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1137\n",
      "I am working on tract number 1140 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1140\n",
      "we have 2 non-opposing shorted wedges for tract no 1141\n",
      "I am working on tract number 1160 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1172\n",
      "I am working on tract number 1180 of 1542 tracts\n",
      "I am working on tract number 1200 of 1542 tracts\n",
      "I am working on tract number 1220 of 1542 tracts\n",
      "I am working on tract number 1240 of 1542 tracts\n",
      "I am working on tract number 1260 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1263\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1271 0 193558.08028259332 2.4927\n",
      "I am working on tract number 1280 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1283\n",
      "we have 2 non-opposing shorted wedges for tract no 1285\n",
      "we have 2 non-opposing shorted wedges for tract no 1295\n",
      "we have 2 non-opposing shorted wedges for tract no 1298\n",
      "we have 2 non-opposing shorted wedges for tract no 1299\n",
      "I am working on tract number 1300 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1305\n",
      "we have 2 non-opposing shorted wedges for tract no 1306\n",
      "we have 2 non-opposing shorted wedges for tract no 1310\n",
      "I am working on tract number 1320 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1325\n",
      "I am working on tract number 1340 of 1542 tracts\n",
      "I am working on tract number 1360 of 1542 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1361\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 8.0 3 86.1 4.491 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 9.0 3 86.1 4.4345 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 10.0 3 86.1 4.3787 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 11.0 3 86.1 4.3236 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 12.0 3 86.1 4.2692 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 13.0 3 86.1 4.2154 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 14.0 3 86.1 4.1624 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 15.0 3 86.1 4.11 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 16.0 3 86.1 4.0583 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 17.0 3 86.1 4.0072 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 18.0 3 86.1 3.9568 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 19.0 3 86.1 3.907 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 20.0 3 86.1 3.8578 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 21.0 3 86.1 3.8093 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 22.0 3 86.1 3.7613 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 23.0 3 86.1 3.714 350908.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 28.0 3 86.1 3.7309 350908.5\n",
      "loop31.0, tr1363,wedgePops645584.96, 23474.0, 345500.0, 23162.9, 253448.1, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0314 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,1.4284\n",
      "loop32.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0314 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,1.2701\n",
      "loop33.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0315 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,-0.053\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 33.0 3 86.1 4.4876 350908.5\n",
      "loop34.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0315 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,-0.0565\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 34.0 3 86.1 4.4311 350908.5\n",
      "loop35.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0315 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,-0.0558\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 35.0 3 86.1 4.3754 350908.5\n",
      "loop36.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0315 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,-0.0551\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 36.0 3 86.1 4.3203 350908.5\n",
      "loop37.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0315 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,-0.0544\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 37.0 3 86.1 4.2659 350908.5\n",
      "loop38.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0315 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,-0.0537\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 38.0 3 86.1 4.2123 350908.5\n",
      "loop39.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0315 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,-0.053\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.78923 23264.5222 23473.9598 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.98532 348709.3516 345500.0086 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.59523 23241.4463 23162.9022 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 4.15925 350908.4641 350908.4641 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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nYWujBlzq4tN/fMLQ686iY/d23DXpZjrtfxgzJ73KI/c8x+Gdfq3f6JqgLX6+JeeffhnzZi3iuovvYPm/lnHakD506bM3Dz4xkR9+/CHuEjOSUYCbWQHQDRifXOfuz7r76ujlG0BB/ZcX7St1WS24ZGjB+29yxtATOKT3njwyewp9ivvz8qPvc+/N02m/Z4e4y5MGYJONW3BKn0G88ugHjL5uCpts3IIh1/6BA3vswuj7buHbFf+Ju8S1yrQDHwUMBdb0+8WpwFM1vWFmA82s1MxKy8rK6l5h1e9ar89L4+fuvPja0xz3h64Un9KJ1958iXNOGcrcmYu46dK72XGHneMuURqgvLw8eh59Ak9PncuDd/2FnXfcnevuvJQO3Xbi+jsv5evl/4y7xBrVGuBmVgwsc/f5a3j/MmA1cH9N77v7OHcvcvei/Px1u8mPnokptVm1ehWPzJ5K1xP34+TBPVjyxWKuPH8E82Yt5pKzryF/y23iLlECYGYccsCRTB/9NE9NeYMuBx3FmCm3ckD3nbno2kEs/uzjuEusIpPLCDsBPczsWGBjoJWZTXX3fmY2ACgGjvAcjG3omZhS3crvV/DAExMYd//tfPn15+y64x6MGv5neh59AhtusGHc5UnA9t69PWNveIC/f76IcfffzkMz7+PBGRM5pktPzhwwpEEMw9Xagbv7MHcvcPdCoC/wYhTevwYuBnq4+/fZLFJT6aW6sm++5qbRV9CheCeG3zqEHVoXct+oJ3hh+gL6FJ+s8JZ607ZNO2645E7mzlzEH39/Ma+VltD9lM4cN/BIXnj1qVjPy63PdeB3AZsBz5nZ22Y2tp5qWiudw2za/v75Ii654Rw6dm/HnRNv4qD9DuXJaMbdkZ2P1XkSyZqtttiai8+6mnmzEsNzS5Z+Sv/zetL1xP149C/3x/LYtzoFuLuXuHtxtNzO3du4+z7Rn0HZKbEytCsm8mgyfZPz9gelDLy4Lwf/9pdMf3ISx3XrR8nD73LviIfYb6+OcZcnTcimLTdj4O8GM+eJjxg1/M+Ul5dz7hW/p1Ov3Rn/4J18/9+VOaslqJmYoCHwpsTdeWnOM/QZdBTdBhzEK3Nf4OwBQ3hj5ifcfNkY2hXuGneJ0oRtuMGG9Ck+meenvcWk2x6nYLsduHLkhexfvBO33HMV3/x7/a66y0RQAV7x27Ea8EZt1epVPPbUA3Q9qYh+53bn0398wuXn3cS8WYsZds51bLPVdnGXKFKhWbNmdD24G4/d+yIzJrxMx306cdu919GhuB2X3TSYz774NGv7DuJmVq4n8jQZr775EhdcdUbFHeNattiUDr86iA8/fpfLbh68xs9lMvad6fh4Rt+VQRdRXzXVZ92ZdD+5rDvj/20NrO61XUlRsH0hXQ48ipLXn2XSw2OY/Og99DzqeEb8aSybbNyi9u+ugyACPElj343fl19/TvPmzdmhdduKdW9/WJq2XSb/Ic/pNhkM7mXUfARYc6ZNVS7rzulxXMM2m7VsVbH8+luvsGz5V/yiYMdav68uggjw6k+l14UGjdfxxf05vrh/3GWIBCHMMXAREQkkwDWRR0QkTRgBnkLnMEVEEoII8PSJPCIiEkSAp1IHLiKSEFSA65mYIiKVggjwKhN54itDRKRBCSLAk5KdtxpwEZFAAlzPxBQRSRdEgIuISLogAtyrDYJrCEVEJJAAFxGRdEEEePUbWKkDFxEJJMBT6RymiEhCEAGe1oFrMr2ISBgBnkoduIhIQjABXmXcWw24iEgYAV71KkK14CIiEEiAg8a9RUSqCyLA9UxMEZF0GQe4mTU3swVmNit63cfMPjCzcjMryl6JIiJSk7p04IOBhSmv3wd+C7xcrxXVxF0TeUREqskowM2sAOgGjE+uc/eF7v63bBW2JrqMUEQkIdMOfBQwFCiv6w7MbKCZlZpZaVlZWV0/DmgqvYhITWoNcDMrBpa5+/x12YG7j3P3Incvys/PX5evqPZ96/0VIiKNQiYdeCegh5ktAaYBh5vZ1KxWVQNNpRcRqarWAHf3Ye5e4O6FQF/gRXfvl/XKqtRQpZ5c7lpEpMFa5+vAzew3ZvYFcCAw28yeqb+yatxfynI29yQiEoa8umzs7iVASbT8OPB4/ZdU035TlnOxQxGRAAQxE1NERNIFEeCpN7DSVHoRkYQgAlxERNIFEeCayCMiki6IAK9CZzFFRIBAAjy9A1cLLiISRICn0jweEZGEYAJcTbeISFVBBLieiSkiki6IABcRkXRBBLi7V5y41EQeEZGEIAJcRETSBRHgDlXuAK4OXEQkkABPpcsIRUQSwghwTeQREUkTRoCnUAcuIpIQTIDrZlYiIlUFEeB6JqaISLogArw6NeAiIoEEeGLyTjSRJ+ZaREQaiiACXERE0gUR4O5eMW6iqfQiIglBBLiIiKQLIsA1lV5EJF0QAQ6warVzw11L+OZfq2jeXAkuIpJxgJtZczNbYGazotdbmNlzZvZJ9HPz7JUJK1b+xOjJSzm6yxYMOrl1NnclIhKEvDpsOxhYCLSKXl8CvODuN5rZJdHri+u5PgCO67Y1m7VsTt+e29C2zSbZ2IWISHAyCnAzKwC6AdcBF0SrewJdouX7gBKyFODt99yM9ntulo2vFhEJVqZDKKOAoUB5yrpt3P2fANHPreu3NBERWZtaA9zMioFl7j5/XXZgZgPNrNTMSsvKytblK0REpAaZdOCdgB5mtgSYBhxuZlOBr81sO4Do57KaPuzu49y9yN2L8vPz66lsERGpNcDdfZi7F7h7IdAXeNHd+wFPAgOizQYAM7JWpYiIpFmf68BvBLqa2SdA1+i1iIjkSF0uI8TdS0hcbYK7fwMcUf8liYhIJoKZiSkiIlUpwEVEAmW5fESZmZUBn+VshwlbActzvM+GTscknY5JzXRc0sVxTH7h7mmX8eU0wONgZqXuXhR3HQ2Jjkk6HZOa6bika0jHREMoIiKBUoCLiASqKQT4uLgLaIB0TNLpmNRMxyVdgzkmjX4MXESksWoKHbiISKOkABcRCVSjDHAz+5WZvW5m75nZTDNrFa3vYGZvR3/eMbPfxF1rLq3luHQ1s/nR+vlmdnjctebKWo7Jlmb2kpmtMLO74q4zl9Z0TKL3hpnZIjP7m5kdHWeduWZm+5jZG1F+lJpZh2j9hmY2MTpe75hZl5wV5e6N7g/wJnBotHwqcE203ALIi5aTt8DNi7veBnBc9gW2j5b3BJbGXWsDOCYtgc7AIOCuuOtsIMdkD+AdYCOgLbAYaB53vTk8Ls8Cx0TLxwIl0fLZwMRoeWtgPtAsFzU1yg4c2BV4OVp+DugN4O7fu/vqaP3GQFM7g7um47LA3b+M1n8AbGxmG8VQXxzWdExWuvurwP/iKixGNR4TEo9RnObuP7j734FFQIcY6ouLU/lM4J8ByX8zewAvALj7MuD/gJxM9GmsAf4+0CNa7gO0Sb5hZh3N7APgPWBQSqA3BWs8Lil6Awvc/YecVRWvTI5JU7OmY9Ia+Dxluy+idU3FecAIM/scuAUYFq1/B+hpZnlm1hbYjxz9ParT7WQbEjN7Hti2hrcuI/Fr3x1mdgWJB0/8mHzT3ecCvzSz3YH7zOwpd280Xda6Hpfos78EbgKOynadubQ+x6SxWsdjYjVs36h+i63luBwBnO/uj5rZ8cCfgSOBCcDuQCmJez3NAXLSGAYb4O5+ZC2bHAVgZrsA3Wr4/EIzW0lizLe0/iuMx7oeFzMrAB4H+rv74uxVmHvr+3elMVrHY/IFVTvLAiqHERqFtR0XM5sMDI5ePgyMjz6zGjg/Zbs5wCdZLLNCoxxCMbOto5/NgD8BY6PXbc0sL1r+BYmxviUxlZlzazkuPwdmA8Pc/bXYCozBmo5JU7aWY/Ik0NfMNoqGCnYG5sVTZSy+BA6Nlg8nCmkza2FmLaPlrsBqd/8wFwUF24HX4kQzOztafgyYGC13Bi4xs1VAOXCWuzelW2Wu6bicA7QDLjezy6N1R0UnZBq7NR0Togd5twI2NLNeJI5JTv5hxqzGY+LuH5jZQ8CHJIYIznb3n2KqMQ5nALdHTeD/gIHR+q2BZ8ysHFgKnJyrgjSVXkQkUI1yCEVEpClQgIuIBEoBLiISKAW4iEigFOAiIoFSgIuIBEoBLiISqP8H4AHG67WxFioAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1363 39.0 3 86.1 4.1592 350908.5\n",
      "loop40.0, tr1363,wedgePops743045.33, 23474.0, 345500.0, 23162.9, 350908.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0315 \n",
      "   targetWP, latest drx4 are tWP,dr, 345045.4,1.1406, 345045.4,-0.0013, 23149.7,-0.0008, 345045.4,-0.0523\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.78923 23264.5222 23473.9598 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.98532 348709.3516 345500.0086 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.59523 23241.4463 23162.9022 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 4.10691 350908.4641 350908.4641 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1363, giving up w pop 743045.3345868606\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1365 10.0 3 90.0 4.1576 337933.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1365 11.0 3 90.0 3.9794 337933.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1365 12.0 3 90.0 3.8088 337933.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1365 13.0 3 90.0 3.6456 337933.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1365 18.0 3 90.0 4.1781 337933.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1365 19.0 3 90.0 3.999 337933.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1365 20.0 3 90.0 3.8276 337933.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1365 21.0 3 90.0 3.6636 337933.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1365 3 337933.9063353146 4.3101\n",
      "we have 2 non-opposing shorted wedges for tract no 1377\n",
      "I am working on tract number 1380 of 1542 tracts\n",
      "I am working on tract number 1400 of 1542 tracts\n",
      "I am working on tract number 1420 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1435 1 221121.87307922254 1.0902\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1435 1 110169.70923427817 0.5451\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1435 1 220979.01514461628 1.0873\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1435 1 138550.3058688884 0.8162\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1435 1 198539.02771913956 0.9518\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1435 1 157045.8184009687 0.884\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1435 1 176097.072481269 0.9179\n",
      "I am working on tract number 1440 of 1542 tracts\n",
      "I am working on tract number 1460 of 1542 tracts\n",
      "I am working on tract number 1480 of 1542 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1488 3 224154.09507370065 1.0691\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1488 3 97875.0842900918 0.5345\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1488 3 224127.98087490734 1.0687\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1488 3 128961.8740645021 0.8016\n",
      "I am working on tract number 1500 of 1542 tracts\n",
      "I am working on tract number 1520 of 1542 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1523 4.0 0 90.0 0.8961 264982.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1523 5.0 0 90.0 0.8381 264982.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1523 6.0 0 90.0 0.7839 264982.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1523 7.0 0 90.0 0.7332 264982.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1523 8.0 0 90.0 0.6858 264982.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1534 1 215845.43601592057 1.0698\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1534 1 104144.29273527955 0.5349\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1534 1 216025.06292604542 1.0735\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1534 1 132165.75437384172 0.8042\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1534 1 189412.3250448241 0.9389\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1534 1 149341.23633669913 0.8715\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1534 1 168230.3748128199 0.9052\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1534 1 177787.82222664662 0.922\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1538 3 484274.44685428275 1.1884\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1538 3 26645.183460672968 0.5942\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1538 3 478409.19473532896 1.1724\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1538 3 330070.13574037596 0.8833\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1538 3 66579.81527391361 0.7387\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1538 3 172667.23804570807 0.811\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1538 3 270458.1722343166 0.8472\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1538 3 234425.23275757657 0.8291\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1538 3 208198.8907990563 0.8201\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1538 3 192383.40759144202 0.8155\n",
      "I am working on tract number 1540 of 1542 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0000304909831914\n",
      "Average and max number of wedgePop loops per tract were:  7.62905317769131 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.010548130642785 7.353349070837357\n",
      "calculated statewide vote was 0.4989505384784719, should have been 0.4968093673346601\n",
      "calcd statewide Hispanic pop was 0.059145710146506, should have been 0.061588361554972576\n",
      "calcd statewide Black pop was 0.05401279271918511, should have been 0.05715326409817228\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.5220492866407264\n"
     ]
    }
   ],
   "source": [
    "#3/7 - 3/10/22 - linear wideAngle, short the opposing wedgePop to match first shorted wedge (see nonper toy)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #NEW 3/7/22\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opposite wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                #now, we \"squish the square\" based on how close we are to boundary - see offline documentation\n",
    "                equivL = (4.*minWedgePop[t] / avgDistrictPop)**0.5  #non-dimensional centroid-boundary distance\n",
    "                wideAngle = avgWedgeAngle*max(  1,( 1.+(maxAngleRatio-1.)*(levelL - equivL)/(levelL - 0.) )  )\n",
    "                wideAngle = min(maxWideAngle, wideAngle)  #prevent too wide of an angle (half-pie)\n",
    "                #HERE, WE ADJUST all wedge ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit to avoid gaping wedges\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                # NEW 3/10/22 - we not only squish the angles, we short the target for the wedge opposite boundary\n",
    "                oppWedgeExpectedPop = minWedgePop[t] * math.tan(0.5*wideAngle)/math.tan(pi/nWedges)  #prediction for new wedge0's pop\n",
    "                oppWedgeExpectedPop = min(oppWedgeExpectedPop,avgDistrictPop/nWedges) #not needed, but just in case...\n",
    "                for nW in range(nWedges):\n",
    "                    if nW == int(nWedges/2):\n",
    "                        targetWedgePop[nW] = oppWedgeExpectedPop\n",
    "                    else:  #NOTE: we don't mess w wedge0's target.  When it shorts again, we will adjust other wedge's tgts\n",
    "                        targetWedgePop[nW] =(avgDistrictPop - oppWedgeExpectedPop)/float(nWedges-1)\n",
    "                # *** END OF 3/10/22 MODIFICATION\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0000304909831914\n",
      "Average and max number of wedgePop loops per tract were:  7.62905317769131 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.010548130642785 7.353349070837357 for  WI\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start WI 2:57.  some interference, finished ~4:15\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WI 19Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"19Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"maxWideAngle\",\"levelL\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, maxWideAngle, levelL]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1363 1.01 ( -90.11489000196185 42.95167516660437 ) 0.3970038282599914 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51 0.0003386830832841626 375 pop,GOP 0.3546666666666667\n",
      "52 4.892859761341989e-05 365 pop,GOP 0.35342465753424657\n",
      "53 0.0 364 pop,GOP 0.3543956043956044\n",
      "54 4.386587405294029e-05 357 pop,GOP 0.35294117647058826\n",
      "180 0.6759324528756573 102 pop,GOP 0.5686274509803921\n",
      "181 0.6738765660194757 378 pop,GOP 0.5291005291005291\n",
      "251 0.695805923403005 1829 pop,GOP 0.5188627665390924\n",
      "252 0.6924125803880592 1515 pop,GOP 0.4772277227722772\n",
      "253 0.6892412583669836 2623 pop,GOP 0.5425085779641632\n",
      "255 0.6942295222776176 1745 pop,GOP 0.526647564469914\n",
      "257 0.6988450557236937 1724 pop,GOP 0.4837587006960557\n",
      "362 0.6986106437111894 961 pop,GOP 0.5702393340270552\n",
      "407 0.6683794030728953 259 pop,GOP 0.7335907335907336\n",
      "411 0.6710124819656407 368 pop,GOP 0.6440217391304348\n",
      "412 0.6679990299553425 155 pop,GOP 0.6451612903225806\n",
      "413 0.6754653350340957 171 pop,GOP 0.6432748538011696\n",
      "414 0.6693120701997547 262 pop,GOP 0.6908396946564885\n",
      "415 0.667666160711909 185 pop,GOP 0.6918918918918919\n",
      "416 0.6611182917606898 226 pop,GOP 0.6902654867256637\n",
      "420 0.6985625537991207 194 pop,GOP 0.7577319587628866\n",
      "424 0.6917046873218549 353 pop,GOP 0.5609065155807366\n",
      "425 0.6989187381050704 327 pop,GOP 0.5596330275229358\n",
      "435 0.6996145913851322 426 pop,GOP 0.5563380281690141\n",
      "438 0.6881023834556499 36 pop,GOP 0.6666666666666666\n",
      "439 0.6724842558757397 246 pop,GOP 0.6422764227642277\n",
      "441 0.6985775949360767 184 pop,GOP 0.6141304347826086\n",
      "443 0.6551458663158322 187 pop,GOP 0.7540106951871658\n",
      "444 0.653191783424058 35 pop,GOP 0.7714285714285715\n",
      "445 0.6845316447667079 441 pop,GOP 0.746031746031746\n",
      "446 0.6793440085452422 61 pop,GOP 0.7377049180327869\n",
      "447 0.6905243433452493 62 pop,GOP 0.7419354838709677\n",
      "1515 0.6798690162414505 263 pop,GOP 0.5247148288973384\n",
      "1516 0.6941885540880941 455 pop,GOP 0.5230769230769231\n",
      "1519 0.6754291618859088 228 pop,GOP 0.43859649122807015\n",
      "1520 0.6418987187511483 142 pop,GOP 0.5633802816901409\n",
      "1521 0.6790372912178277 126 pop,GOP 0.8015873015873016\n",
      "1528 0.6600747687151867 426 pop,GOP 0.539906103286385\n",
      "1529 0.6823997638142514 430 pop,GOP 0.4558139534883721\n",
      "1530 0.6903604421989569 374 pop,GOP 0.4893048128342246\n",
      "1532 0.6462110382022401 265 pop,GOP 0.4641509433962264\n",
      "1533 0.6796771944294941 371 pop,GOP 0.444743935309973\n",
      "1534 0.6867847558571848 315 pop,GOP 0.44761904761904764\n",
      "1535 0.6864119071685336 372 pop,GOP 0.6263440860215054\n",
      "1540 0.6641590519098758 552 pop,GOP 0.4673913043478261\n",
      "1541 0.6739488984873245 96 pop,GOP 0.46875\n",
      "1542 0.6527476124763498 873 pop,GOP 0.3940435280641466\n",
      "1543 0.6672600571037209 173 pop,GOP 0.3930635838150289\n",
      "1544 0.6550258966717587 423 pop,GOP 0.39243498817966904\n",
      "1545 0.6712088181428961 355 pop,GOP 0.4225352112676056\n",
      "1546 0.6768895023170088 549 pop,GOP 0.4225865209471767\n",
      "1547 0.671847816152802 474 pop,GOP 0.4219409282700422\n",
      "1548 0.6449516177772606 899 pop,GOP 0.39377085650723026\n",
      "1549 0.6557232443831341 525 pop,GOP 0.3942857142857143\n",
      "1550 0.658046402975691 125 pop,GOP 0.424\n",
      "1551 0.6573442639482162 352 pop,GOP 0.42329545454545453\n",
      "1552 0.6575733752913124 324 pop,GOP 0.4228395061728395\n",
      "1553 0.6708550479752572 630 pop,GOP 0.4222222222222222\n",
      "1554 0.6507510376938983 714 pop,GOP 0.36554621848739494\n",
      "1555 0.6494182587454933 210 pop,GOP 0.3619047619047619\n",
      "1556 0.6516829940415488 110 pop,GOP 0.36363636363636365\n",
      "1557 0.6456519962943066 279 pop,GOP 0.3655913978494624\n",
      "1558 0.6436375839088991 558 pop,GOP 0.34587813620071683\n",
      "1559 0.638474800451154 234 pop,GOP 0.34615384615384615\n",
      "1560 0.6396685205244963 111 pop,GOP 0.34234234234234234\n",
      "1561 0.6406272060868607 184 pop,GOP 0.3641304347826087\n",
      "1562 0.637612694054706 856 pop,GOP 0.3644859813084112\n",
      "1563 0.6449372103250529 356 pop,GOP 0.3651685393258427\n",
      "1564 0.6349623008503154 991 pop,GOP 0.39959636730575177\n",
      "1565 0.6272050820238163 58 pop,GOP 0.39655172413793105\n",
      "1566 0.6291186204261772 260 pop,GOP 0.4\n",
      "1567 0.6355133060016824 150 pop,GOP 0.4\n",
      "1568 0.6267182015253853 1045 pop,GOP 0.3990430622009569\n",
      "1569 0.6411804203919955 213 pop,GOP 0.39906103286384975\n",
      "1570 0.6376392423995445 161 pop,GOP 0.39751552795031053\n",
      "1571 0.6336977368966166 221 pop,GOP 0.3438914027149321\n",
      "1572 0.6420908717820613 161 pop,GOP 0.34782608695652173\n",
      "1573 0.641873377758035 508 pop,GOP 0.3464566929133858\n",
      "1574 0.6746885400706607 435 pop,GOP 0.4436781609195402\n",
      "1575 0.6516253254855674 787 pop,GOP 0.5209656925031766\n",
      "1576 0.6926861942560758 650 pop,GOP 0.5215384615384615\n",
      "2443 0.6976258981772875 537 pop,GOP 0.40409683426443205\n",
      "2445 0.6907989968878945 1044 pop,GOP 0.33620689655172414\n",
      "2447 0.6993237773494626 404 pop,GOP 0.27970297029702973\n",
      "2448 0.6922420215953727 441 pop,GOP 0.3424036281179138\n",
      "2449 0.6864241546279768 615 pop,GOP 0.37886178861788616\n",
      "2469 0.6993281342129813 277 pop,GOP 0.30324909747292417\n",
      "2470 0.6985705959653628 26 pop,GOP 0.11538461538461539\n",
      "2471 0.6942695417862331 428 pop,GOP 0.45093457943925236\n",
      "2472 0.6841440490409195 811 pop,GOP 0.4217016029593095\n",
      "2473 0.6973961026698172 256 pop,GOP 0.2421875\n",
      "2474 0.6824551230561983 739 pop,GOP 0.39377537212449254\n",
      "2475 0.6734392869626663 1057 pop,GOP 0.40397350993377484\n",
      "2476 0.6748448116386986 652 pop,GOP 0.4831288343558282\n",
      "2477 0.6737565535069097 298 pop,GOP 0.40268456375838924\n",
      "2478 0.672630287321351 466 pop,GOP 0.4699570815450644\n",
      "2479 0.6741157295255787 430 pop,GOP 0.4558139534883721\n",
      "2480 0.6679256278295335 848 pop,GOP 0.4540094339622642\n",
      "2481 0.6828979187593277 553 pop,GOP 0.45569620253164556\n",
      "2494 0.6969391267081131 837 pop,GOP 0.4551971326164875\n",
      "2495 0.6950340209417605 56 pop,GOP 0.5357142857142857\n",
      "2496 0.690100004076389 1013 pop,GOP 0.4906219151036525\n",
      "2497 0.6699772285330915 835 pop,GOP 0.48383233532934133\n",
      "2498 0.6717531855314558 430 pop,GOP 0.5\n",
      "2499 0.670332864216726 919 pop,GOP 0.4548422198041349\n",
      "2501 0.6993734644438285 698 pop,GOP 0.47277936962750716\n",
      "2502 0.6842505935248895 34 pop,GOP 0.47058823529411764\n",
      "2503 0.6804908133478564 1309 pop,GOP 0.4851031321619557\n",
      "2504 0.6805002153638058 753 pop,GOP 0.4395750332005312\n",
      "2524 0.698107844118556 462 pop,GOP 0.4935064935064935\n",
      "2554 0.6621300451045713 1097 pop,GOP 0.544211485870556\n",
      "2555 0.6664142614674098 1053 pop,GOP 0.5441595441595442\n",
      "2556 0.6691275610159895 903 pop,GOP 0.5437430786267996\n",
      "2557 0.6588449859230223 873 pop,GOP 0.5853379152348225\n",
      "2558 0.6505275831724829 734 pop,GOP 0.5858310626702997\n",
      "2559 0.6889136034525949 1164 pop,GOP 0.5146048109965635\n",
      "2560 0.681399204453569 865 pop,GOP 0.5144508670520231\n",
      "2561 0.6656198500935331 737 pop,GOP 0.5983717774762551\n",
      "2562 0.661099797146511 1317 pop,GOP 0.59832953682612\n",
      "2563 0.6887703770323501 1239 pop,GOP 0.5980629539951574\n",
      "2629 0.6837880538419285 711 pop,GOP 0.5541490857946554\n",
      "3212 0.05019675242517697 913 pop,GOP 0.5706462212486308\n",
      "3213 0.6551508523380362 931 pop,GOP 0.5714285714285714\n",
      "3214 0.1307730381304225 924 pop,GOP 0.5703463203463204\n",
      "3241 0.6900403267631086 717 pop,GOP 0.606694560669456\n",
      "3253 0.6855362311244492 766 pop,GOP 0.6161879895561357\n",
      "3550 0.43471983392455366 1614 pop,GOP 0.2224287484510533\n",
      "3599 0.2860887071789915 1625 pop,GOP 0.20553846153846153\n",
      "4550 0.6748003488959528 406 pop,GOP 0.687192118226601\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.7 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of 29 tracts that were used less than 0.7 of expectation in WI\n"
     ]
    },
    {
     "data": {
      "image/png": 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KVOBKKX/gK+BN4OWsAv8GmK61XpffEPZW4K+tf423fnkLH3cfLl6/mHNwRrta7dgyYou8NRUO4djFY7z363tsPbGV2MTYXF9X17su+5/fz5J9S3hs6WM5+6zfbGSrkVSrWI3HQx6nrnddgJxfCDKdaJyiFvgS4L+ABzA+q8BjgGVAD+BG1vLf7/C1I4GRALVr126dkJBQlO0otNT0VP6z6T+sO7aOAK8Adifu5siFIwxoPIAlDy/h/PXz7D+3n+sZ1wmtEUrl8pUNySlEUWRYMziYfBCzxUyQTxDbT23n6IWjTNk8hYTLt/7f69WgF/fVvg+vcl4E+QTRY14P0ixpOc8H+wVTy7MWG45voLpHdcbeO5YRISOo4FqhpDerzCt0gSulegO9tNbPKaUi+LPA9wAbgDFAG2AxUFfnsUIjRuBpGWnEJsbyyrpX2Bi/kfa125N4LZHDFw7j4+7Dvuf24VvBt0QzCVHSzqeeZ8zqMfi6++KknHi46cN/2fvlfOp5tp/aTrWK1Vh7bC2L9izi4o2LtKreipNXTrL91HYA7vW/l+ZVm7PjzA7MFjOTIyZzMPkg/p7+PNz0YTnpVzEoSoH/FxhG5geV5QBPYCngA7yttY7Met1R4F6t9bnc1lVSBX784nGeXP4kTsqJqBNROeeGHh82nne7vQvAjYwbuDi5yGHNQuSD1prPfv+M0atGU8GlAs5Ozli0hWvma3957X117uPle18m2C+YQO9AA9KWPkX+EDNrJRH8OQJ/Bqihtf6XUqohsB6obeQI/NjFY4xYNoLNCZuBzBMN/a3V3+gY0JH2tdtT3aN6sX1vIcqi01dPc/zicTzdPDl99TRjVo/h4PmDOc9/1usznm3zrIEJS4fcCrwo+4HPAmZlTaWYgcfzKu/idPnGZUavGs382PloNI+3eJwJ4RMyT0IkhCg2NTxqUMOjBgDN/ZpzYPQBLt24xKAlg1hzdA3f7vuWocFD8XTzNDhp6VSgEXhRFdcI/MFFD/LjoR8ZFzaOF+95kYt7T7ArMpKWERE0Dwuz+fcTQuTtzNUz1Hg/s9i71evGz4/9bHAix1YcI3DDmS1mJkVOYtnBZbze4XWmdJpCXFQUL3buTLrZjIurKx+vXy8lLkQJyrBmsHDPwpzHjzZ71MA0pZtDF/j3+7/nv7/8lxZ+LZjQbgIAuyIjSTebsVospJvN7IqMlAIXogQNWjKIpfuX0jmwM/MHzJczKhYjhy7w7A8rx947NmeOrWVEBC6urjkj8JYREQYmFKLsqehaEYDdibvZnLAZH3cfNidsJv5yPGeuniG0RihvdHrD4JSlg8POgR9IPkDTz5rSL6gfCx9aiJuzW85zcVFRMgcuhEEsVgvToqfx2obXuJx2OWd5Oedy3Mi4AcCUjlPo36g/18zX8HH3oa53XTnyOQ822Y2wqGxR4CnmFObFzmPiuok4KSdin4mlVqVaNkoohLCVq2lXOXLhCJduXMLTzZPWNVqzIG4BQ5cOvePrezXoxfeDvpeLRN+Bwxf4icsneG7lc6w6vAqLttC+dnvm9JtDvcr1cl4TFxXFjshIWsvIWwi7lWJOYcPxDTkH2O1N2ssXO74gMSWRvkF9mRA+gbY120qR38ThC/zp5U8zc9dMRrUexeBmg7m/zv23vOWKi4ri+Zv2PvlU9j4RwmFYtZWXVr/EjJ0zcqZZkickU8W9isHJ7ENuBe4wpxebFzuPHvV7MK33NCICIv4yX7bjpr1PMsxmdkRGGhNUCFFgTsqJj3t+zNlxZwmvFQ5Ak8+acOnGJWOD2TmHKPBD5w+RZkmja92uub6mddbeJyaTCWdXV1rL3idCOJyfDv/Eryd+BTIvEu3i5GJwIvvmELsR/nwk8yiuhxo/lOtrmoeF8en69TIHLoQD83XPPDOom8mNqKei5NS1d+EQI/DscxRnX2g4N83Dwnji1VelvIVwUF3rdWX+gPmkW9Op93E9Ei4Zc/0AR+EQBZ59cYVtJ7eRmp4KZJ7e8kraFQw6f5YQohikW9J5sNGDjL13LKnpqWw7uc3oSHbNIaZQwmuF42ZyY8jSIQBUr1gdq7aSmJJIC78WjG47mmHBw245mEcI4Xh6LejFumOZV2ms6FqR7vW7G5zIvjnECLyRTyN2jNzB0y2fBuAe/3voXr87kyMmo9H87ce/0fKLljIaF8LBtfD780Liq4euxqucl3FhHIDD7AeeG601A78dyNL9Szkz7gzVKlaz6fqFECVr4tqJvPvruxwafYgGVRoYHccuOPx+4LlRSvH3dn/H3cWdjl915MzVM0ZHEkIUwcAmAwGYHzff4CT2z+ELHKBNzTasHrqaE5dP0GJaC9779T2upF0xOpYQooB2ntnJK+teAeDBRg8aG8YBOMSHmPnRoU4HNo/YzKgVo5iwdgJbT2zl+0HfGx1LCJEPVm1l/JrxfLDtAzxcPZjZdyYh1UKMjsXnv3/OrJhZACgUTsoJpbL+ROV6P/t12fc93DyY2nMqvhV8bZqv1BQ4QKvqrfj9b7/Td2Ff4hLjjI4jhMin6NPRfLDtAzoHduabh7/J2XXYSF/v/prnVj5Hy2otqe6Rueeb1hqNzrlv1dacx1arlQwy/vLclbQrHEg+wJMhT9p8r5pSVeCQ+RYsLikON5PsUiiEo8i+CISryfWO5X3h+gW+3fstAV4BdKvXrdjPHb7m6BqeWv4UnQM7s3LoyiKdGXHN0TV0n9e9WDKXmgLXWvPtvm8ZtGQQXuW8mNl3ptGRhBD5cCPjBk0/awpAj/o9bnnOqq3Mi53Hcz89R0p6CpD5Iefc/nMp51yuWPLsPLOTh755iCa+Tfjuke+KVN6R8ZEM+W4Ildwq0dS3qQ1TZio1Bb7h+AYGLRmEj7sP257adst5woUQ9mty5OSc+1M2T+HDbR9yI+MG3uW9ib8UT2p6KvfVuY93u77LVzFf8Vn0ZwRXDeaf9//T5lnWHF3DsO+H4V3Om1VDV1GpXKVCrUdrzae/f8pLq1+iQZUGLBu8jJqeNW2ctpQUuNaa1UdWAzCjzwwpbyEcyOBmg9l2ahuR8ZEkpybTqnor/D38OZtylpoeNXmgwQM81+Y5XEwueJXz4rPoz4hJjLFphitpVxi/Zjwzds6gkU8jlj6ylBoeNQq1rrSMNJ5f+Twzd82kT8M+zBswL+eavbbm8AUedSKKKZunsOrIKvo07EPP+j2NjiSEKIAW1Vqw8fGN3Mi4gVVbcXdxv+PrfvnjF+6fcz+Vy1fm3a7v2uz7rzu2jqeWP8XJKyeZED6ByRGTKe9SvlDrunTjEr0X9Gbria283uF1JnecjJMqvr21HbrAD50/RPiszJO/v9/tfV6850VMTiaDUwkhCiOvOe3k1GT6LOxDoFcgSx5ZQl3vukX+flfSrjBhzQSm75xOUJUgfhnxC2G1Cn8m0/Op5+k2rxtxiXEsHriYR5o+UuSMd+PQBZ79afVDjR9ibNhYg9MIIWwpJiqK3yMjaRMRwafnZnHpxiVWPLrCJvuH3zzqHh82nv90/E+hR90ASSlJdPm6C4cvHGbZ4GX0bFAyMwEOXeBmixmANjXaGJxECGFLMVFRPNW5M2azmRsNTBwZbKZH/R60q92u0OtMTk1mYdxCvo79mujT0TYZdUPmLo5d53blyIUjrHh0BZ3rdi7S+grCoQtckblfZVySHLQjRGnye2QkZrMZi7ZwsqMFgE96flKode06s4vJmybz0+GfyLBmEFIthA+6f8Co1qOKNOqGzGmYnvN7ciD5QImXNzh4gX+9+2sA+jfqb3ASIYQttYmIwNXVlfN10rjhZyWsSlvqV65f4PVorRm6dCiJKYmMuWcMw1sMJ9gv2CYZU9NT6b2gNzvP7GTpI0vpWi/3a/YWF4c+mdV9de7DSTmxZP8So6MIIWwoJCyMmevXM3bYa7goF2Kv7GXWrllYrJYCrScuKY79yft5s9ObvNftPZuVd1pGGv0X92fria3M6z+PPkF9bLLegnLoAg+rFUbHgI7sPrvb6ChCCBsLCQtjzD/+w6YRmwj2C+ap5U8ROiOU+Evx+V7Hoj2LMClTnhdEL6h0SzqDlgxizdE1zOw7k0HNBtls3QXl0AUOEOAVwP7k/ew7t8/oKEKIYhBWK4ytT27l/W7vE3M2hpZftOTr3V8TczYmzxG51pqFexbSuW5nm54F8M0tb7Ls4DI+7fUpT4Q8YbP1FobDzoFfM1/jP5v+w8xdM/Eq55VzMhwhROmjlGJs2FiqVqjK0z8+zeM/PJ7znJvJjYZVGhLgFUCdSnWo41WH+pXrU6V8FeIvxTM5YnIeay64nWd2EuwXzHNtnrPpegvDIQs8OTWZ0OmhJFxOYETICN7q/JZcSk2IMmBo8FD6N+7P8YvHafZ5MwDSLGnEJcX9ZW80f09/yjmXs/mFIRJTEu2mb/I9haKUMimldimlVmQ9nqSUOqWUism69Sq+mLc6dvEYCZcTqOVZi1favWI3P0whRPFzd3En4XICAOG1wulStws1PW49UZSzkzPnUs7Rp2Efm5+HJPFaIn4V/Gy6zsIqyAh8DLAfuPmn8YHW+j3bRrq7NjXa8F7X9xi/djytprfi2IvH8KtoHz9QIUTxizkbA8DaYWtxd3EnOTUZgOMXj7M/eT/v/foee5L28OI9L9r0+06LnkbC5QSa+Dax6XoLK18jcKWUP/AA8GXxxskfpRTjwsflfIAg899ClC3ZF2yxWC1sSdhCwIcBhM8Mx2wx4+PuQ1xSHOPCxtG+dnubfc9Fexbx3E/P8UCDBxgXNs5m6y2K/I7APwQmAh63LR+tlBoORAPjtNYXb/9CpdRIYCRA7dq1C5/0DlLTU3F3cS+2E7sLIYyjtSYuKY7jF49z4soJTlw+QXJqMlas7E3aC8DaY2sZ/v1wanrWJC0jjQ6zO1DRtSLNqzbnjU5v2CzLqsOrGPb9MDrU6cC3D3+Li8nFZusuirsWuFKqN5Cktd6hlIq46anPgSmAzvrzf8CTt3+91no6MB0gNDRUFz3yn+6rfR/f7P2G96PeZ0K7CbZctRDCYIv2LGLI0iE5j11Nrvi4++Ds5JxzitbBSwZTr3I9Nj6+kQouFXhl3Sss2beEuf3n4uZcuMsqWqwWjl48CmSeruPwhcMM/GYgwX7BLB+8vMiH39uS0jrvTlVK/RcYBmQA5cicA1+qtX7sptcEACu01s3yWldoaKiOjo4uauZbNJraiACvAFY/ttqm6xVCGOuz3z/j+ZXPs+axNQT7BeNbwTenuDce38gDCx4g0DuQDcM33PIZmNa60NefvHj9IvfNuY89SXtuWR5UJYgtI7bY/Kry+aWU2qG1Dr19+V1H4FrrV4FXs1YSAYzXWj+mlKqutT6T9bL+wJ47r6H4zNgxgyMXjtj0KCshhH1pUa0FVStUzXm84fgGei/oTV3vuqwfvv4vOzAUtrznxMxhxLIRAPhV8ON/3f6HRqNQdK/fHR93n8JvRDEpyn7g7yilQsicQokHRtkiUEF89NtHWLRFzgUuRBmx7tg6+izsQ/3K9Vk/fP0txV5Ua46uAWBW31mMaDnCZustTgUqcK11JBCZdX9YMeQpkAZVGrDv3D5MSq7CI0RplT3Nu/boWvou6kuDyg1YP3y9TaYzUtNTWXl4Jd8f+J6FexYC0LSq7a8eX1wc8kjMbOWdy+NqcsWrnJfRUYQQNpZ9vn/IHB33XdiXIJ8g1g9fb5PpDK013ed155c/fsHH3YcnQ55kcLPBtK3ZtsjrLikOW+AXr1/kx0M/0r52+0LPeQkh7N/PR39m5I8jaezbmHXD1lHFvUqR12nVViZFTuKXP37hnS7vMDZsLM5OjleHjpeYzN+cr294nWvma7zV+S2j4wghitGIZSNo4deCtcPW2qS8LVYLA74ZwPKDyxneYrjDljc46OlkfzjwA59Ff8azoc/K9TCFKOVCqoWwbrhtRt4AS/YtYfnB5Tzf5nnm9JvjsOUNDlrg2Wcdu55xnc0Jm7Fqq8GJhBC21imwE6Naj2LdsHVULl/ZJutMMafwwqoXaOLbhHe7vuvw068O+atnXNg4klKSmB0zmzkxc6jpUZN/3f8vnm71dM6O/kIIxxbkE8S03tNsus6vdn/FudRzfD/oe7s6orKwHLLtKrhWYGqvqSSNT2LhQwup612XUStG0WF2h78cQSWEEACHzh/i9Q2vE+YfRnitcKPj2IRDFni2Cq4VGNxsMJue2MScfnM4mHyQll+0ZNzP4wp03TwhROmWlpFGn4V9cHZyZv6A+Q4/dZLNoQs8m1KKx0Me58DoAzwW/Bgf/vYhdT+qy6gfR8n8uBCCf278J4fOH2J2v9kEegcaHcdmSkWBZ/Nx92F2v9nEj4lnQOMBTN85nZk7Z0qJC1FGpaanMvLHkbz767s80/oZHmj4gNGRbKpUFXi2WpVq5ZxwfeSKkZj+Y+LBRQ+Smp5qcDIhREm5dOMSLaa1YMbOGYwPG8+nD3xqdCSbc8i9UPIjrFYYx8cc58eDPxKZEMnS/Uup/H+V+Vurv9HYtzGV3CrRpW4X/Cr6kWHNwKRMaLTsxSJEKTFl0xSOXDjCssHL6BvU1+g4xeKu5wO3peI4H3h+9V3Ylx8P/UhF14pcM1/LWe7j7pNzPT3IPGl8oFcgI0JGMD58PCYnOVGWEI5m37l9NP+8OX9r9Teb74pohEKfD7y0WDZ4GdczrlPeuTynrp4i8VoiPx/9mbikOIKqBOW87nr6dbaf3s7f1/8dgFfav2JUZCFKvT1RUeyMjKRVRATNwsJstt4pm6dQ3rm8TS+rZo/KTIErpXB3cQfA39Mff09/WtdofcfXaq3pMrcLUzZPYWK7iaVmlyMh7MmeqChGR0SQkZ6Os4sLUyMjbVLiCZcSWLxnMRPCJ9jlRRhsSSZ870ApxZMhT5KSnsKTy5/k4vW/XKtZCFFEK7/+mnSzGa016WYzK7/+2ibrXXVkFRrNwCYDbbI+eyYFnoshzYfweofX+SrmK/w/8OefG/5JijnF6FhClBq3v6+1xftcq7by4bYPaV29NaE1/jJlXOpIgedCKcWUTlOIeSaGfkH9eGPLG1R9ryptZ7TliR+e4J2t77Di0ArOpZwzOqoQDqnn8OG4uLmhlMLFzY2ew4cXeZ2xibEcPH+Q59s8XyamPsvMXihFtfWPrXyz9xv2Je9jb9JezlzLvJ6zSZkIrxVO17pdaezbmAcbPejQp6cUoiTZ+kPM19a/xlu/vMWRF45Qr3I9GyS0D7nthSIFXkgXr19k77m9rDq8ipVHVhJzNgbI/ID0iRZP8ETIE6XqH5AQ9i4tI43K71Smb1BfFj600Og4NpVbgcsUSiF5l/emfe32vNn5TXaN2sW1V6/xw6AfCPYL5q1f3qL+J/Vp+ElDvoj+gpL8JSlEWbUpYROp6an0rN/T6CglRkbgxeDUlVPMi53H8kPL+fXEr/Ss35P/dv4vLaq1MDqaEKWSVVup93E90i3pHBh9gIquFY2OZFMyAi9BNT1r8kr7V9gyYgvvdn2XqJNR9JjfgytpV4yOJkSp9PORn4m/FM+kiEmlrrzzIgVejJyUE+PDx9Ozfk/OXjvLrjO7jI4kRKmUkp65i2+Dyg0MTlKypMBLQD3vzA8zh/8wnKtpVw1OI0Tp06VuF6pWqMqEtROwWC1GxykxUuAlYEqnKczqO4s/Lv/BzF0zjY4jRKnjVc6Lj3p8xO+nf2fq9qlGxykxUuAlJPt0lpsSNhmcRIjSaVDTQfSo34N/R/6bDGuG0XFKhBR4CalcvjJDmw/lhwM/MHX7VNIy0oyOJESpopQi3D+cy2mXpcCFbSml+F+3/9GyWkteWPUCtT6oxfKDy42OJUSpcfH6RRbtXURQlSDKOZczOk6JkAIvQX4V/dgxcgdrh62lVqVa9FvUj9DpoXy47UMu37hsdDwhHJJVW1l5eCXhs8I5fP4wH/b40OhIJUYO5DHIjYwbfP775yzYs4Do09E4OzlT17sufRr24a3Ob+FqcjU6ohB27dD5Q0zdPpVFexZxLvUc9bzr8fkDn9O1Xlejo9mcnAvFju04vYPv9n/HjjM7WHN0DZ0CO/FRj49oVrWZ0dGEsDtmi5mnlz/N3Ni5uDi5MKDxAPo07MPDTR8utQOfMn9JNXvWukZrWtdojVVb6TGvB+uPr6fFtBZ82P1DXrjnBaPjCWFX5sXOY27sXMbcM4ZX27+KX0U/oyMZJt9z4Eopk1Jql1JqxW3LxyultFKqdF+7qAQ4KSfWDFtD4vhEejXoxYurXyR8Zjjf7P2mzHyqLsTdbDi+Aa9yXrzf/f0yXd5QsA8xxwD7b16glKoFdAX+sGWoss7H3YeljyxlSscpnL12lkFLBtF9Xnfmxc5j+6ntnL12VgpdlElJKUlExkcS4BWAk5J9MPI1B66U8ge+At4EXtZa985avgSYAiwDQrXWyXmtR+bAC85itfB59OdMXDuR6xnXc5Y7KSc61O5A6+qtae7XnO71ulPdo7qBSYUoXhnWDLrN7UbUySiinooipFqI0ZFKTFHnwD8EJgIeN62wL3BKa727LFy6yCgmJxOj245mVOtRHL5wmKMXjvLH5T84ceUEyw4u45Ptn5BuTaeCSwVWP7aa9rXbGx1ZiGLxRfQXbIzfyJx+c8pUeeflriNwpVRvoJfW+jmlVAQwHngE2Ah001pfVkrFk8sIXCk1EhgJULt27dYJCQk23YCyzqqtxCbG8uCiBzmXeo4ZfWbwaLNHy8T1AEXZcs+X92CxWogeWfbexRflfODtgL5ZJb0I6ATMBQKB3VnL/YGdSqlqt3+x1nq61jpUax3q6+tbhE0Qd+KknAipFkLUU1E08mnE0KVD6TK3C2eunjE6mhA28+n2T9l+ajt9GvYxOopdKdB+4Nkj8Ow58JuWxyNz4IazWC1M3zGdsT+PJbxWOCuGrMDdxd3oWEIUidaaqu9VpWGVhmwYvgE3ZzejI5U4uSJPGWByMvFsm2d5v/v7RMZH0mF2B3af3W10LCGKZHfibpJTk3ky5MkyWd55KVCBa60jbx99Zy0PuNvoW5Sc59o8x+KBizl15RTd53WX86wIh7bswDKclFPOKZnFn2QEXko93PRhFg1cRHJqMsHTgolNjDU6khCFcunGJdxMblQqV8noKHZHCrwUiwiIYMuILWRYMxixbITRcYQolCCfIK5nXOfstbNGR7E7UuClXFitMCaET2DnmZ1ExkcaHUeIAmvk0wiAqBNRBiexP1LgZcDI1iOpXrE6g5cM5vD5w0bHEaJAOtTuQKBXIK+se4VzKeeMjmNXpMDLAHcXd97u8jaJKYlsO7nN6DhCFIjJycTigYtJuJzAO1vfMTqOXZECLyN2n92NSZno16if0VGEKLAL1y8A4O/pb3AS+yIFXgZcunGJ6Tunc3/A/Xi6eRodR4gCu5J2BYC2NdsanMS+SIGXAXuS9nDNfI0X275odBQhCiW7uGV32FtJgZcBcYlxADT2bWxwEiEKZ3bMbAA5ZfJtpMDLgPqV6wPw/f7vDU4iRMF9EPUBkzdNZniL4fRu+JcDwcs0KfAyINA7EMi8ircQjmTGjhm8vOZlBjYZyMy+M+UqPLeRn0YZUKV8FRr5NGJ2zGw++e0To+MIkS9b/9jKqBWj6NWgF/MHzMfZSa7Bfjsp8DLAu7w3vz39Gy2qteCln18ixZxidCQh8nQj4wZPLX+KOl51WDxwMa4mV6Mj2SX5lVZGeLp5cs18DXcXd8o5lzM6jhB5enr50xw8f5Dlg5dT0bWi0XHslozAy5D6leuTbkln3bF1RkcRIk/rjq3D2cmZbvW6GR3FrkmBlyFf9vmSyuUr86/IfxkdRYg8pVvTGdlqpFzA4S6kwMuQ6h7VCfAKyDmqTQh7FJsYy6Ubl+T83/kgBV6GLIxbSNTJKML8w4yOIsQd3ci4wbDvh+Hr7svLYS8bHcfuSYGXIcF+wbi7uLPh+AZOXD5hdBwhbnHNfI3+i/sTmxjLzL4z8XH3MTqS3ZMCL0Oa+zVn8xObSUpJYlLkJKPjCJHjatpVQqeHsvboWr7s8yUPNHzA6EgOQXYjLGMCvALwcPPgxBUZgQv74fl25lkylz6ylP6N+xucxnHICLyMmbFzBkkpSfh7+ss1BoVduHk678FGDxoXxAFJgZcxjzR9hKoVqjI7ZjY1/leDdrPaMX3HdDk6Uxhmw/ENAMQ9G4dSyuA0jkUKvIyp612Xk2NPsnPkTiZFTOJK2hVGrRhF7Q9r8/ORn42OJ8qgzQmb8S7nTRPfJkZHcThS4GWQi8mFltVb8q/7/0XsM7FsGbGFWp616LWgl5xyVpSopJQkvt33LV3rdZUzDRaC/MTKOKUU7Wu355cnf6F51eYM+GYA209tNzqWKCO+3PklV81XmRwx2egoDkkKXABQ0bUig5sNBuDz6M8NTiPKio3xG2ns05hGPo2MjuKQpMAFaRlp/GfTf/h35L+pXak248LGGR1JlBGBXoEcu3iMg8kHjY7ikKTAyzizxUz4rHD+Hflv+jfqz86RO2lWtZnRsUQZ8ff2fyfNksazPz2L2WI2Oo7DkQIv48b9PI6dZ3Yyq+8sFg1cRBX3KkZHEmVIoFcgjwU/xsb4jXKa40KQAi+jLt+4zJhVY5j6+1SeDX2WES1HGB1JlEFKKdrWaAuAu4u7wWkcjxR4GfXmljf5ZPsnjGw1kre7vG10HFGGfbDtA/w9/bm/zv1GR3E4UuBl1Omrp6nrXZcv+nyBp5un0XFEGdaudjtOXjnJ27/IQKKgpMDLqJT0FHnLKuzCzL4zAfjHhn+QcCnB4DSOJd8FrpQyKaV2KaVWZD2eopSKVUrFKKXWKKVqFF9MYWsZ1gw58k3Yhf/75f8AcDW5otEGp3EsBfkfPAbYf9Pjd7XWwVrrEGAFIBdadCCBXoEcvnCYq2lXjY4iyrDI+Mica7Qmjk8kwCvA2EAOJl8FrpTyBx4AvsxeprW++cKKFUB+dTqSwc0Gk5qeyrf7vjU6iijDQqqFAJmj7+jT0caGcUD5HYF/CEwErDcvVEq9qZQ6AQwllxG4UmqkUipaKRV97ty5omQVNhTmH0Yjn0ZMi56G1vK7VxjDq5wXyROSqVOpDl3nduXZFc8aHcmh3LXAlVK9gSSt9Y7bn9Nav6a1rgXMB0bf6eu11tO11qFa61BfX98iBxa2oZTihbYv8Pvp39lx5i9/tUKUmCruVdg5aiedAjsxbcc0Oay+APIzAm8H9FVKxQOLgE5KqXm3vWYB8JCNs4lilj3fKBdzEEar6FqRd7q8A8Dec3sNTuM47lrgWutXtdb+WusAYDCwQWv9mFKqwU0v6wscKKaMopjU9a4LZF5mLd2SbnAaUdZlH4+QlJJkcBLHUZT9yN5WSu1RSsUC3cjcS0U4kJoeNanlWYv5cfNp9GkjYs7GGB1JlGHnUjM/I7t4/aLBSRxHgQpcax2pte6ddf8hrXWzrF0J+2itTxVPRFFcPNw8OPriUZY+spRzKeeYsnmK0ZFEGebh6gFkHmQm8keO5CjjXEwu9G/cn/sD7mfp/qUylSIM09yvOX2D+vLmljeZF3v7x2ziTqTABQDNfDPPAT565R13JhKiRMzuN5t2tdox/PvhfPzbx7KL611IgQsAJkVMIqRaCJsSNhkdRZRhlctXZu2wtfQN6suY1WPoOb8nRy8cNTqW3ZICFwC4ObsxoNEADp4/KCcUEoYq71KeJY8sYWL4RH4++jPP/iQH9+RGClzkaFe7HQABHwUQfyne2DCiTHN2cubU1VMoFJMiJhkdx25JgYsc99W5jzD/MABCpoXw+obX5ag4YZiU9BTqeNUhvFa40VHslhS4yOHs5MyWEVtYP3w9zao24+1f3qbFtBaMXT2W4xePcyXtinyoJEpEuiWd3Wd3U79yfaOj2DVnowMI+2JyMtEpsBOdAjtx5uoZXl3/Kp9s/4QPf/sQgHre9ejdsDdta7Yl2C+Y+pXrU865nLGhRalisVp4avlTHL90nI97fmx0HLumSnJEFRoaqqOj5ZSRjubE5RN8f+B7UswpbErYxKaETdzIuAGAk3KiTqU6BPkEEVQliIZVGtLCrwVtarbB1eRqcHLhiGbtmsVTy59iWPAwvu7/tdFx7IJSaofWOvQvy6XARUGlW9I5kHyAPUl7OHj+IAfPH+TQ+UMcTD6YcxSdv6c/64evp2GVhganFY7mvtn38cflPzg+5jhKKaPj2IXcClymUESBuZhcaO7XnOZ+zW9ZrrXm9NXTRJ2M4sllTzL257H8NOQng1IKRxQVtY/dx/ZRNb0G27btICzsL50lbiIfYgqbUUpR07MmA5sM5JV2r7Dy8EoWxC0wOpZwAAunT6dT02D6hnfmWuJ1jp8/So/7HiIqSt6x50UKXBSLMfeOIcw/jKFLh7J0/1Kj4wg7tnD6dCaMGsWhfXG4cRa3s6lYKmXgnXGdb7+eb3Q8uyYFLopFRdeKbHh8AyHVQhi6dKgcGCRytfK772557GQFlJXrVKUiZmNCOQiZAxfFppxzOb575DvqfVyPwI8CmRA+AYXK+WDq9vuQOQ2T130n5VQsN5OTqdjWXdibs5MzJmUq9R/kBYW0ZeOaNTmPtQVw1VRQSQwY/phxwRyAFLgoVhVdK+bc/2T7JzkHAmn0Lfch80PQO90v6xQKk5Mpp9Bvv29SmY+dlBNWbcWiLVi19Y637J/rnf682c0/+5ufu/3v5E57sd3pF29uy0zKhNk9g9RxTjhZrWBVWLw1YKHj6HaEhIXZ6sdYKkmBi2JVtUJV9L9tU8TZBZRbORX0llfRGXmzWC05+TKsGVisFizagsWa9fgO9y0682ZSme8ksv/MvimV+e7l5jK90583yy5c4Jbnbl5++3N3+iWc1zKrtnLy9FmWr9iA0mmUd7oGZ8DZYmLQ8yNs8u+mNJMCFw7DSTmBAhMmo6MIG4tqEkNk5Hb8q7iRev40bSMiaCmj77uSAhdCGC4sLISwsBCjYzgc2QtFCCEclBS4EEI4KClwIYRwUFLgQgjhoKTAhRDCQUmBCyGEg5ICF0IIB1WiF3RQSp0DEvLxUh8guZjjFLfSsA1QOrajNGwDyHbYk5Lehjpaa9/bF5ZogeeXUir6TlefcCSlYRugdGxHadgGkO2wJ/ayDTKFIoQQDkoKXAghHJS9Fvh0owPYQGnYBigd21EatgFkO+yJXWyDXc6BCyGEuDt7HYELIYS4CylwIYRwUHZT4EqpFkqpKKVUnFLqR6WUZ9bytkqpmKzbbqVUf6Oz5iWP7eiqlNqRtXyHUqqT0Vlzk8c2VFFKbVRKXVNKTTU6593kth1Zz72qlDqilDqolOpuZM67UUqFKKW2Zf0fiFZKtc1a7qqUmp21fbuVUhHGJs1dHtvgopT6Kmsb9iulXjU6a17y2I6hN/VUjFLKqpQKKfZAWmu7uAG/A/dn3X8SmJJ13x1wzrpfHUjKfmyPtzy2oyVQI+t+M+CU0VkLsQ0VgPbAM8BUo3MWYTuaALsBNyAQOAqYjM6bx3asAXpm3e8FRGbdfx6YnXW/KrADcDI6bwG3YQiwKOu+OxAPBBidt6DbcdtrmgPHSiKP3YzAgSBgc9b9tcBDAFrrVK11RtbycmD3V7rNbTt2aa1PZy3fC5RTSrkZkC8/ctuGFK31L8ANo4IV0B23A+hHZmmkaa2PA0eAtgbkyy8NZL97qARk/ztqAqwH0FonAZcAww8uyUVu26CBCkopZ6A8YAaulHy8fMttO272KLCwJMLYU4HvAfpm3X8YqJX9hFLqHqXUXiAOeOamQrdHuW7HTR4Cdmmt00osVcHkZxscQW7bURM4cdPrTmYts1cvAe8qpU4A7wHZ0wy7gX5KKWelVCDQGvv9u3qJO2/DEiAFOAP8Abyntb5gSML8eYk7b8fNBlFCBV6i18RUSq0Dqt3hqdfIfIv7sVLqX8ByMn8TA6C1/g1oqpRqDHyllFqltTZsFFjY7cj62qbA/wHdijtnXoqyDfakkNuh7vB6Q9/Z3WU7OgNjtdbfKaUeAWYCXYBZQGMgmsxzDP0KGDa4KeQ2tAUsQA3AG9iilFqntT5WQrH/opDbkf219wCpWus9JRLW6DmlXOaZGgLbc3luIxBqdMbCbAfgDxwC2hmdrSh/F8ATOMAceG7bQeao6dWbnvsZCDM6Yx7ZL/PnMRsKuJLL634FmhidtyDbAHwKDLvpdbOAR4zOW9i/C+AD4B8llcduplCUUlWz/nQCXgemZT0OzJofQylVh8x5zXiDYt5VHtvhBfxEZnFsNSxgPuS2DY4mj+1YDgxWSrllTT00ALYbkzJfTgP3Z93vBBwGUEq5K6UqZN3vCmRorfcZE/Gu7rgNZE6bdFKZKgD3AgcMyJdfuW1H9r+zh4FFJZbG6N9oN/3mGkPm6PQQ8DZ//pYbRuaHfjHATuBBo7MWcjteJ3OuL+amW1Wj8xZkG7KeiwcuANfInDu2yxFfPrbjNTL3PjlI1l4F9nojc8+fHWTOef8GtM5aHpCVfz+wjsxTjhqet4DbUBH4Nuv/+D5ggtFZC7MdWc9FANtKMo8cSi+EEA7KbqZQhBBCFIwUuBBCOCgpcCGEcFBS4EII4aCkwIUQwkFJgQshhIOSAhdCCAf1/199f/PKT2sVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        counter +=1\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "print(\"map of {2} tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE, counter) )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.3 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.3\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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jrFPZkm6WzSt0AVdKdQO6aK2fVUq14e8Cvg/YAIwGmgALgBo6jwMa0QNPSU8hKj6Kl9e9zMbYjbTwb0H89XiOXDyCt6s3B549gE9ZnxLNJERJu5B8gdGrRuPj6oOdsuPRkEf/cfbLheQL7Dy9k0pulVh7fC3z983n0s1L3Ff5Pk5dPcXO0zsBeMDvARpUbMCus7tIzUhlYpuJxCTG4Ofhx6Mhj8qkX8WgKAX8P8BgMr+odAE8gMWAN/C+1npT1vOOAQ9orc/ndqySKuAnLp1g+LLh2Ck7tp/cnjM39Lhm4/ig4wcA3Ey/iaOdo1zWLEQ+aK354o8vGLVyFGUdy+Jg50CGzuB66vV/PLdV9Va89MBLhPqGEugVaEDa0qfIX2JmHaQNf/fAnwaqaK3fVErVAdYD/kb2wI9fOs6wpcPYErcFyJxo6Kn7nqJtQFta+LegsnvlYvvZQtiiM9fOcOLSCTycPThz7QyjV40m5kJMzuNfdPmCZ5o8Y2DC0iG3Al6U88CnA9OzhlJSgaF5Fe/idOXmFUatHMWcqDloNEPDhjI+YnzmJERCiGJTxb0KVdyrANDAtwGHRh3i8s3L9FvUjzXH1rDwwEIGhg7Ew9nD4KSlU4F64EVVXD3wh+c/zM+Hf2Zss7G8cP8L+Hn4mf1nCCHy7+y1s1T5KLOwd6zZkdWDVhucyLoVRw/ccKkZqby96W2Wxizl9ZavM6ndJKMjCWHz0k3pzNs3L+f+Y/UfMzBN6WbVBXzJwSX859f/EOYbxvjm442OI4QA+i3qx+KDi3kw8EHm9J4jMyoWI6su4NlfVo55YIyMsQlhIdyc3ADYG7+XLXFb8Hb1ZkvcFmKvxHL22lnCq4TzTrt3DE5ZOlhtAT+UeIivdn1Fr+Be9K/f3+g4Qogs03tMp2mVpry24TX6Luqbs9/FwYWb6TdZfWw1Lg4u9AruxfXU63i7elPDq4Zc+VwIVvclZlJqErOjZjNh3QTslB1RT0dRrVw1MyUUQpjLtZRrHL14lMs3L+Ph7EHjKo2ZGz2XgYsH3vX5XWp3YUm/JbJI9F2Y5TzwoipKAT955STPrniWlUdWkqEzaOHfgpk9Z1KzfE0zpxRCFKek1CQ2nNiQc4Hd/oT9fL3ra+KT4ukR1IPxEeNpWrWpFPJbWH0Bf3LZk0z7cxojG4+kf/3+tK7eWj5yCVFKmLSJF1e9yDe7v+Fm+k0AEscnUsG1gsHJLIPVF3CXd1xoG9iWlQNXmjmVEMJSXLl5hS5zu/Dbyd+oWLYiMaNi8HTxNDqW4XIr4FYxP+ThC4dJyUihQ40ORkcRQhSjX478wm8nfwMyF4l2tHM0OJFls4oCvvpo5lVcj9R9xOAkQoji5OOaOTOos70z25/YLlPX3oNVFPDsOYqzFxoWQpROHWp2YE7vOaSZ0qj5aU3iLhuzfoC1sIoCnr24wo5TO0hOSwYyp7e8mnIVg+bPEkIUg7SMNB4OfpgxD4whOS2ZHad2GB3JolnFhTwR1SJwtndmwOIBAFR2q4xJm4hPiifMN4xRTUcxOHQwzg7OBicVQhRFl7ldWHc8c5VGNyc3OtXqZHAiy2YVPfBg72B2jdjFk42eBOB+v/vpVKsTE9tMRKN56uenaPR1I+mNC2Hlwnz/Xkh81cBVcgbKPVjNaYS50VrTZ2EfFh9czNmxZ6nkVsmsxxdClKwJayfwwW8fcHjUYWpXqG10HItg1acR5kUpxSvNX8HV0ZW237Xl7LWzRkcSQhRBn3p9AJgTPcfgJJbP6gs4QJOqTVg1cBUnr5wk7KswPvztQ66mXDU6lhCigHaf3c3L614G4OHgh40NYwWs4kvM/GhZvSVbhm1h5PKRjF87nm0nt7Gk3xKjYwkh8sGkTYxbM46Pd3yMu5M703pMo2GlhkbH4ss/vmT6nukAKBR2yg6lsv5E5bqd/bzsbXdnd6Z0noJPWR+z5is1BRzgvsr38cdTf9BjXg+i46ONjiOEyKfIM5F8vONjHgx8kB8e/SHn1GEjfb/3e55d8SyNKjWisnvmmW9aazQ6Z9ukTTn3TSYT6aT/47GrKVc5lHiI4Q2Hm/2smlJVwCHzI1h0QjTO9nJKoRDWInsRCCd7p7sW74s3LrJw/0ICPAPoWLNjsU9kt+bYGp5Y9gQPBj7IioErijQz4ppja+g0u1OxZC41BVxrzcIDC+m3qB+eLp5M6zHN6EhCiHy4mX6TkC9CAHio1kO3PWbSJmZHzebZX54lKS0JyPySc1avWbg4uBRLnt1nd/PID49Qz6ceP/b9sUjFe1PsJgb8OIByzuUI8QkxY8pMpaaAbzixgX6L+uHt6s2OJ3bIPOFCWImJmybmbE/aMonJOyZzM/0mXmW8iL0cS3JaMq2qt+KDDh/w3Z7v+CLyC0IrhvJG6zfMnmXNsTUMXjIYLxcvVg5cSTmXcoU6jtaaz//4nBdXvUjtCrVZ2n8pVT2qmjltKSngWmtWHV0FwDfdv5HiLYQV6V+/PztO72BT7CYSkxO5r/J9+Ln7cS7pHFXdq9K1dleebfIsjvaOeLp48kXkF+yJ32PWDFdTrjJuzTi+2f0Nwd7BLO67mCruVQp1rJT0FJ5b8RzT/pxG9zrdmd17drGt2Wv1BXz7ye1M2jKJlUdX0r1OdzrX6mx0JCFEAYRVCmPj0I3cTL+JSZtwdXS96/N+/etXWs9sTfky5fmgwwdm+/nrjq/jiWVPcOrqKcZHjGdim4mUcSxTqGNdvnmZbnO7se3kNl5v+ToT207EThXf2dpWXcAPXzhMxPQIAD7q+BEv3P8C9nb2BqcSQhRGXmPaicmJdJ/XnUDPQBb1XUQNrxpF/nlXU64yfs14pu6eSlCFIH4d9ivNqjUr9PEuJF+g4+yORMdHs6DPAvqG9L33i4rIqgt49rfVj9R9hDHNxhicRghRXF5d9yqXb15m+WPLzXJ++K297nHNxvHvtv8udK8bICEpgfbft+fIxSMs7b+UzrVLZiTAqgt4akYqAE2qNDE4iRCiuKw/vp5v//yWh2o9RHP/5oU+TmJyIvOi5/F91PdEnok0S68bMk9x7DCrA0cvHmX5Y8t5sMaDRTpeQVh1AVdknlcZnSAX7QhRGqVlpDFmdean6886f1aoY/x59k8mbp7IL0d+Id2UTsNKDfm408eMbDyySL1uyByG6TynM4cSD5V48QYrL+Df7/0egF7BvQxOIoQoDquPrSY6IZqONTtSq3ytAr9ea83AxQOJT4pn9P2jGRI2hFDfULNkS05Lptvcbuw+u5vFfRfToWbJr9lr1ZNZtareCjtlx6KDi4yOIoQoBi38W+Bk78S2v7Yx/c/pZJgyCvT66IRoDiYe5N127/Jhxw/NVrxT0lPotaAX205uY3av2XQP6m6W4xaUVRfwZtWa0TagLXvP7TU6ihCiGHi6eLJp6CZCfUN5YtkThH8TTuzl2Hy/fv6++dgre7MuiJ6WkUa/Rf1Yc2wN03pMo1/9fmY7dkFZdQEHCPAM4GDiQQ6cP2B0FCFEMWhWrRnbhm/jo44fsefcHhp93Yjv937PnnN78uyRa62Zt28eD9Z40KyzAL679V2Wxizl8y6f83jDx8123MKw2jHw66nX+ffmfzPtz2l4unjmTIYjhCh9lFKMaTaGimUr8uTPTzL0p6E5jznbO1OnQh0CPAOoXq461T2rU6t8LSqUqUDs5VgmtpmYx5ELbvfZ3YT6hvJsk2fNetzCsMoCnpicSPjUcOKuxDGs4TDee/A9WUpNCBswMHQgver24sSlE9T/sj4AKRkpRCdE/+NsND8PP1wcXMy+MER8UrzF1Jt8D6EopeyVUn8qpZZn3X9bKXVaKbUn69al+GLe7vil48RdiaOaRzVebv6yxfwyhRDFz9XRlbgrcQBEVIugfY32VHW/faIoBzsHziedp3ud7mafhyT+ejy+ZX3NeszCKkgPfDRwELj1t/Gx1vpD80a6tyZVmvBhhw8Zt3Yc9029j+MvHMfXzTJ+oUKI4rfn3B4A1g5ei6ujK4nJiQCcuHSCg4kH+fC3D9mXsI8X7n/BrD/3q8iviLsSRz2femY9bmHlqweulPIDugLfFm+c/FFKMTZibM4XCDL+LYRtyV6wJcOUwda4rQRMDiBiWgSpGal4u3oTnRDN2GZjaeHfwmw/c/6++Tz7y7N0rd2Vsc3Gmu24RZHfHvhkYALgfsf+UUqpIUAkMFZrfenOFyqlRgAjAPz9/Quf9C6S05JxdXQttondhRDG0VoTnRDNiUsnOHn1JCevnCQxORETJvYn7Adg7fG1DFkyhKoeVUlJT6HljJa4ObnRoGID3mn3jtmyrDyyksFLBtOyeksWProQR3tHsx27KO5ZwJVS3YAErfUupVSbWx76EpgE6Kw//wcMv/P1WuupwFSA8PBwXfTIf2vl34of9v/AR9s/Ynzz8eY8tBDCYPP3zWfA4gE5953snfB29cbBziFnitb+i/pTs3xNNg7dSFnHsry87mUWHVjErF6zcHYo3LKKGaYMjl06BmRO13Hk4hH6/NCHUN9QlvVfVuTL781JaZ13TVVK/QcYDKQDLmSOgS/WWg+65TkBwHKtdf28jhUeHq4jIyOLmvk2wVOCCfAMYNWgVWY9rhDCWF/88QXPrXiONYPWEOobik9Zn5zCvfHERrrO7UqgVyAbhmy47TswrXWh15+8dOMSrWa2Yl/Cvtv2B1UIYuuwrWZfVT6/lFK7tNbhd+6/Zw9ca/0q8GrWQdoA47TWg5RSlbXWZ7Oe1gvYd/cjFJ9vdn3D0YtHzXqVlRDCsoRVCqNi2Yo59zec2EC3ud2o4VWD9UPW/+MEhsIW75l7ZjJs6TAAfMv68r+O/0OjUSg61eqEt6t34RtRTIpyHvh/lVINyRxCiQVGmiNQQXzy+ydk6AyZC1wIG7Hu+Dq6z+tOrfK1WD9k/W2FvajWHFsDwPQe0xnWaJjZjlucClTAtdabgE1Z24OLIU+B1K5QmwPnD2CvZBUeIUqr7GHetcfW0mN+D2qXr836IevNMpyRnJbMiiMrWHJoCfP2zQMgpKL5V48vLlZ5JWa2Mg5lcLJ3wtPF0+goQggzy57vHzJ7xz3m9SDIO4j1Q9abZThDa02n2Z349a9f8Xb1ZnjD4fSv35+mVZsW+dglxWoL+KUbl/j58M+08G9R6DEvIYTlW31sNSN+HkFdn7qsG7yOCq4VinxMkzbx9qa3+fWvX/lv+/8yptkYHOysrxxaX2Iy3zlf3/A611Ov896D7xkdRwhRjIYtHUaYbxhrB681S/HOMGXQ+4feLItZxpCwIVZbvMFKp5P96dBPfBH5Bc+EPyPrYQpRyjWs1JB1Q8zT8wZYdGARy2KW8VyT55jZc6bVFm+w0gKePevYjfQbbInbgkmbDE4khDC3doHtGNl4JOsGr6N8mfJmOWZSahLPr3yeej71+KDDB1Y//GqVbz1jm40lISmBGXtmMHPPTKq6V+XN1m/y5H1P5pzoL4SwbkHeQXzV7SuzHvO7vd9xPvk8S/otsagrKgvLKqtdWaeyTOkyhYRxCcx7ZB41vGowcvlIWs5o+Y8rqIQQAuDwhcO8vuF1mvk1I6JahNFxzMIqC3i2sk5l6V+/P5sf38zMnjOJSYyh0deNGLt6bIHWzRNClG4p6Sl0n9cdBzsH5vSeY/VDJ9msuoBnU0oxtOFQDo06xKDQQUz+fTI1PqnByJ9Hyvi4EII3Nr7B4QuHmdFzBoFegUbHMZtSUcCzebt6M6PnDGJHx9K7bm+m7p7KtN3TpIgLYaOS05IZ8fMIPvjtA55u/DRd63Q1OpJZlaoCnq1auWo5E66PWD4C+3/b8/D8h0lOSzY4mRCipFy+eZmwr8L4Zvc3jGs2js+7fm50JLOzyrNQ8qNZtWacGH2Cn2N+ZlPcJhYfXEz5/yvPU/c9RV2fupRzLkf7Gu3xdfMl3ZSOvbJHo+UsFiFKiUmbJ3H04lGW9l9Kj6AeRscpFvecD9ycimM+8PzqMa8HPx/+GTcnN66nXs/Z7+3qnbOeHmROGh/oGciwhsMYFzEOezuZKEsIa3Pg/AEafNmAp+57yuynIhqh0POBlxZL+y/lRvoNyjiU4fS108Rfj2f1sdVEJ0QTVCEo53k30m6w88xOXln/CgAvt3jZqMhCiEKatGUSZRzKmHVZNUtkMwVcKYWroysAfh5++Hn40bhK47s+V2tN+1ntmbRlEhOaTyg1pxwJYQviLsexYN8CxkeMt8hFGMxJBnzvQinF8IbDSUpLYviy4Vy68Y+1moUQFmrl0ZVoNH3q9TE6SrGTAp6LAQ0G8HrL1/luz3f4fezHGxveICk1yehYQog8mLSJyTsm07hyY8Kr/GPIuNSxmSGUglJKMandJB4NeZT3f32fd7a+w0c7PiLEJ4R6PvVybvdXvd+whU6FELeLio8i5kIM03tMt4mhTyng9xDqG8rcR+byXJPn+GH/DxxIPMCaY2v4bu93ANgreyKqRdChRgfq+tTl4eCHrXp6SiGs2cL9CwFoVb2VwUlKhs2cRmhul25cYv/5/aw8spIVR1ew59weIPML0sfDHufxho9Ts3xNY0MKYUNS0lMo/9/y9AjqwbxH5hkdx6xyO41QxsALyauMFy38W/Dug+/y58g/uf7qdX7q9xOhvqG89+t71PqsFnU+q8PXkV9Tkm+SQtiqzXGbSU5LpnOtzkZHKTHSAy8Gp6+eZnbUbJYdXsZvJ3+jc63O/OfB/xBWKczoaEKUSiZtouanNUnLSOPQqEO4ObkZHcmspAdegqp6VOXlFi+zddhWPujwAdtPbeehOQ9xNeWq0dGEKJVWH11N7OVY3m7zdqkr3nmRAl6M7JQd4yLG0blWZ85dP8efZ/80OpIQpVJSWuYpvrXL1zY4ScmSAl4Canplfpk55KchXEu5ZnAaIUqf9jXaU7FsRcavHU+GKcPoOCVGCngJmNRuEtN7TOevK38x7c9pRscRotTxdPHkk4c+4Y8zfzBl5xSj45QYKeAlJHs6y81xmw1OIkTp1C+kHw/Veoi3Nr1Fuind6DglQgp4CSlfpjwDGwzkp0M/MWXnFFLSU4yOJESpopQiwi+CKylXpIAL81JK8b+O/6NRpUY8v/J5qn1cjWUxy4yOJUSpcenGJebvn09QhSBcHFyMjlMipICXIF83X3aN2MXawWupVq4aPef3JHxqOJN3TObKzStGxxPCKpm0iRVHVhAxPYIjF44w+aHJRkcqMXIhj0Fupt/kyz++ZO6+uUSeicTBzoEaXjXoXqc77z34Hk72TkZHFMKiHb5wmCk7pzB/33zOJ5+npldNvuz6JR1qdjA6mtnldiGPFHALsOvMLn48+CO7zu5izbE1tAtsxycPfUL9ivWNjiaExUnNSOXJZU8yK2oWjnaO9K7bm+51uvNoyKOltuNj80uqWbLGVRrTuEpjTNrEQ7MfYv2J9YR9FcbkTpN5/v7njY4nhEWZHTWbWVGzGH3/aF5t8Sq+br5GRzJMvsfAlVL2Sqk/lVLL79g/TimllVKle+2iEmCn7FgzeA3x4+LpUrsLL6x6gYhpEfyw/web+VZdiHvZcGIDni6efNTpI5su3lCwLzFHAwdv3aGUqgZ0AP4yZyhb5+3qzeK+i5nUdhLnrp+j36J+dJrdidlRs9l5eifnrp+Tgi5sUkJSAptiNxHgGYCdknMw8jUGrpTyA74D3gVe0lp3y9q/CJgELAXCtdaJeR1HxsALLsOUwZeRXzJh7QRupN/I2W+n7Gjp35LGlRvTwLcBnWp2orJ7ZQOTClG80k3pdJzVke2ntrP9ie00rNTQ6Eglpqhj4JOBCYD7LQfsAZzWWu+1haWLjGJvZ8+opqMY2XgkRy4e4djFY/x15S9OXj3J0pilfLbzM9JMaZR1LMuqQato4d/C6MhCFIuvI79mY+xGZvacaVPFOy/37IErpboBXbTWzyql2gDjgL7ARqCj1vqKUiqWXHrgSqkRwAgAf3//xnFxcWZtgK0zaRNR8VE8PP9hzief55vu3/BY/cdsYj1AYVvu//Z+MkwZRI6wvU/xRZkPvDnQI6tIzwfaAbOAQGBv1n4/YLdSqtKdL9ZaT9Vah2utw318ZPFfc7NTdjSs1JDtT2wn2DuYgYsH0n5We85eO2t0NCHM5vOdn7Pz9E661+ludBSLUqDzwLN74Nlj4Lfsj0XGwA2XYcpg6q6pjFk9hohqESwfsBxXR1ejYwlRJFprKn5YkToV6rBhyAacHZyNjlTiZEUeG2BvZ88zTZ7ho04fsSl2Ey1ntGTvub1GxxKiSPbG7yUxOZHhDYfbZPHOS4EKuNZ6052976z9AffqfYuS82yTZ1nQZwGnr56m0+xOMs+KsGpLDy3FTtnlTMks/iY98FLq0ZBHmd9nPonJiYR+FUpUfJTRkYQolMs3L+Ns70w5l3JGR7E4UsBLsTYBbdg6bCvppnSGLR1mdBwhCiXIO4gb6Tc4d/2c0VEsjhTwUq5ZtWaMjxjP7rO72RS7yeg4QhRYsHcwANtPbjc4ieWRAm4DRjQeQWW3yvRf1J8jF44YHUeIAmnp35JAz0BeXvcy55POGx3HokgBtwGujq683/594pPi2XFqh9FxhCgQezt7FvRZQNyVOP677b9Gx7EoUsBtxN5ze7FX9vQM7ml0FCEK7OKNiwD4efgZnMSySAG3AZdvXmbq7qm0DmiNh7OH0XGEKLCrKVcBaFq1qcFJLIsUcBuwL2Ef11Ov80LTF4yOIkShZBduOR32dlLAbUB0fDQAdX3qGpxEiMKZsWcGgEyZfAcp4DagVvlaACw5uMTgJEIU3MfbP2bi5okMCRtCtzr/uBDcpkkBtwGBXoFA5ireQliTb3Z9w0trXqJPvT5M6zFNVuG5g/w2bECFMhUI9g5mxp4ZfPb7Z0bHESJftv21jZHLR9Kldhfm9J6Dg52swX4nKeA2wKuMF78/+TthlcJ4cfWLJKUmGR1JiDzdTL/JE8ueoLpndRb0WYCTvZPRkSySvKXZCA9nD66nXsfV0RUXBxej4wiRpyeXPUnMhRiW9V+Gm5Ob0XEslvTAbUit8rVIy0hj3fF1RkcRIk/rjq/Dwc6BjjU7Gh3FokkBtyHfdv+W8mXK8+amN42OIkSe0kxpjLhvhCzgcA9SwG1IZffKBHgG5FzVJoQlioqP4vLNyzL/dz5IAbch86Lnsf3Udpr5NTM6ihB3dTP9JoOXDMbH1YeXmr1kdByLJwXchoT6huLq6MqGExs4eeWk0XGEuM311Ov0WtCLqPgopvWYhrert9GRLJ4UcBvSwLcBWx7fQkJSAm9vetvoOELkuJZyjfCp4aw9tpZvu39L1zpdjY5kFeQ0QhsT4BmAu7M7J69KD1xYDo/3M2fJXNx3Mb3q9jI4jfWQHriN+Wb3NyQkJeDn4SdrDAqLcOtw3sPBDxsXxApJAbcxfUP6UrFsRWbsmUGV/1Wh+fTmTN01Va7OFIbZcGIDANHPRKOUMjiNdZECbmNqeNXg1JhT7B6xm7fbvM3VlKuMXD4S/8n+rD662uh4wgZtiduCl4sX9XzqGR3F6kgBt0GO9o40qtyIN1u/SdTTUWwdtpVqHtXoMreLTDkrSlRCUgILDyykQ80OMtNgIchvzMYppWjh34Jfh/9Kg4oN6P1Db3ae3ml0LGEjvt39LddSrzGxzUSjo1glKeACADcnN/rX7w/Al5FfGpxG2IqNsRup612XYO9go6NYJSnggpT0FP69+d+8tekt/Mv5M7bZWKMjCRsR6BnI8UvHiUmMMTqKVZICbuNSM1KJmB7BW5veoldwL3aP2E39ivWNjiVsxCstXiElI4VnfnmG1IxUo+NYHSngNm7s6rHsPrub6T2mM7/PfCq4VjA6krAhgZ6BDAodxMbYjTLNcSFIAbdRV25eYfTK0Uz5YwrPhD/DsEbDjI4kbJBSiqZVmgLg6uhqcBrrIwXcRr279V0+2/kZI+4bwfvt3zc6jrBhH+/4GD8PP1pXb210FKsjBdxGnbl2hhpeNfi6+9d4OHsYHUfYsOb+zTl19RTv/yodiYKSAm6jktKS5COrsAjTekwD4F8b/kXc5TiD01iXfBdwpZS9UupPpdTyrPuTlFJRSqk9Sqk1SqkqxRdTmFu6KV2ufBMW4f9+/T8AnOyd0GiD01iXgvwPHg0cvOX+B1rrUK11Q2A5IAstWpFAz0COXDzCtZRrRkcRNmxT7KacNVrjx8UT4BlgbCArk68CrpTyA7oC32bv01rfurBiWZC3TmvSv35/ktOSWXhgodFRhA1rWKkhkNn7jjwTaWwYK5TfHvhkYAJgunWnUupdpdRJYCC59MCVUiOUUpFKqcjz588XJaswo2Z+zQj2DuaryK/QWt57hTE8XTxJHJ9I9XLV6TCrA88sf8boSFblngVcKdUNSNBa77rzMa31a1rrasAcYNTdXq+1nqq1Dtdah/v4+BQ5sDAPpRTPN32eP878wa6z//irFaLEVHCtwO6Ru2kX2I6vdn0ll9UXQH564M2BHkqpWGA+0E4pNfuO58wFHjFzNlHMsscbZTEHYTQ3Jzf+2/6/AOw/v9/gNNbjngVca/2q1tpPax0A9Ac2aK0HKaVq3/K0HsChYsooikkNrxpA5jJraRlpBqcRti77eoSEpASDk1iPopxH9r5Sap9SKgroSOZZKsKKVHWvSjWPasyJnkPw58HsObfH6EjChp1PzvyO7NKNSwYnsR4FKuBa601a625Z249oretnnUrYXWt9ungiiuLi7uzOsReOsbjvYs4nnWfSlklGRxI2zN3JHci8yEzkj1zJYeMc7R3pVbcXrQNas/jgYhlKEYZp4NuAHkE9eHfru8yOuvNrNnE3UsAFAPV9MucAH7XiricTCVEiZvScQfNqzRmyZAif/v6pnOJ6D1LABQBvt3mbhpUasjlus9FRhA0rX6Y8awevpUdQD0avGk3nOZ05dvGY0bEslhRwAYCzgzO9g3sTcyFGJhQShirjWIZFfRcxIWICq4+t5plf5OKe3EgBFzma+zcHIOCTAGIvxxobRtg0BzsHTl87jULxdpu3jY5jsaSAixytqreimV8zABp+1ZDXN7wuV8UJwySlJVHdszoR1SKMjmKxpICLHA52DmwdtpX1Q9ZTv2J93v/1fcK+CmPMqjGcuHSCqylX5UslUSLSMtLYe24vtcrXMjqKRXMwOoCwLPZ29rQLbEe7wHacvXaWV9e/ymc7P2Py75MBqOlVk251utG0alNCfUOpVb4WLg4uxoYWpUqGKYMnlj3Bicsn+LTzp0bHsWiqJHtU4eHhOjJSpoy0NievnGTJoSUkpSaxOW4zm+M2czP9JgB2yo7q5aoT5B1EUIUg6lSoQ5hvGE2qNsHJ3sng5MIaTf9zOk8se4LBoYP5vtf3RsexCEqpXVrr8H/slwIuCiotI41DiYfYl7CPmAsxxFyI4fCFw8QkxuRcRefn4cf6IeupU6GOwWmFtWk1oxV/XfmLE6NPoJQyOo5FyK2AyxCKKDBHe0ca+DaggW+D2/ZrrTlz7QzbT21n+NLhjFk9hl8G/GJQSmGtzlw7Q3P/5lK880G+xBRmo5SiqkdV+tTrw8vNX2bFkRXMjZ5rdCxhZcqXKc/Ri0eNjmEVpICLYjH6gdE082vGwMUDWXxwsdFxhBUJ8w3j5JWTRsewClLARbFwc3Jjw9ANNKzUkIGLB8qFQSLfHOwcyNAZRsewCjIGLoqNi4MLP/b9kZqf1iTwk0DGR4xHoXLGNu/chsxhmLy27ZRdsdzs7eyL7diFvTnYOWCv7G1uLNjZwZmk1CS01jbX9oKSAi6KlZuTW872Zzs/y7kQSKNv24bML0Hvtm3rFAp7O/ucgn7ntr3KvG+n7DBpExk6A5M23fWW/Xu925+3uvV3f+tjd/6d3O0stru98ea2Lzv7rbcTl08AsP3UdrkK8x6kgItiVbFsRfRb5inE2QUot+JU0Ftehc7IW4YpIydfuimdDFMGGTqDDFPW/btsZ+jMm73K/CSR/Wf2TanMTy+3FtO7/Xmr7IIL3PbYrfvvfOxub8J57TNpE+mm9Ntujas0xtXRNWfNVpE7KeDCatgpO1Bgj73RUYSwCPIlphBCWCkp4EIIYaWkgAshhJWSAi6EEFZKCrgQQlgpKeBCCGGlpIALIYSVkgIuhBBWqkQXdFBKnQfi8vFUbyCxmOMUt9LQBigd7SgNbQBphyUp6TZU11r73LmzRAt4fimlIu+2+oQ1KQ1tgNLRjtLQBpB2WBJLaYMMoQghhJWSAi6EEFbKUgv4VKMDmEFpaAOUjnaUhjaAtMOSWEQbLHIMXAghxL1Zag9cCCHEPUgBF0IIK2UxBVwpFaaU2q6UilZK/ayU8sja31QptSfrtlcp1cvorHnJox0dlFK7svbvUkq1MzprbvJoQwWl1Eal1HWl1BSjc95Lbu3IeuxVpdRRpVSMUqqTkTnvRSnVUCm1I+v/QKRSqmnWfiel1Iys9u1VSrUxNmnu8miDo1Lqu6w2HFRKvWp01rzk0Y6Bt9SpPUopk1KqYbEH0lpbxA34A2idtT0cmJS17Qo4ZG1XBhKy71viLY92NAKqZG3XB04bnbUQbSgLtACeBqYYnbMI7agH7AWcgUDgGGBvdN482rEG6Jy13QXYlLX9HDAja7sisAuwMzpvAdswAJifte0KxAIBRuctaDvueE4D4HhJ5LGYHjgQBGzJ2l4LPAKgtU7WWqdn7XcBi1/pNrd2/Km1PpO1fz/gopRyNiBffuTWhiSt9a/ATaOCFdBd2wH0JLNopGitTwBHgaYG5MsvDWR/eigHZP87qgesB9BaJwCXAcMvLslFbm3QQFmllANQBkgFrpZ8vHzLrR23egyYVxJhLKmA7wN6ZG0/ClTLfkApdb9Saj8QDTx9S0G3RLm24xaPAH9qrVNKLFXB5KcN1iC3dlQFTt7yvFNZ+yzVi8AHSqmTwIdA9jDDXqCnUspBKRUINMZy/65e5O5tWAQkAWeBv4APtdYXDUmYPy9y93bcqh8lVMBLdFFjpdQ6oNJdHnqNzI+4nyql3gSWkflODIDW+ncgRClVF/hOKbVSa21YL7Cw7ch6bQjwf0DH4s6Zl6K0wZIUsh3qLs839JPdPdrxIDBGa/2jUqovMA1oD0wH6gKRZM4x9BtgWOemkG1oCmQAVQAvYKtSap3W+ngJxf6HQrYj+7X3A8la630lEtboMaVcxpnqADtzeWwjEG50xsK0A/ADDgPNjc5WlL8L4HGsYAw8t3aQ2Wt69ZbHVgPNjM6YR/Yr/H3NhgKu5vK834B6RuctSBuAz4HBtzxvOtDX6LyF/bsAPgb+VVJ5LGYIRSlVMetPO+B14Kus+4FZ42MopaqTOa4Za1DMe8qjHZ7AL2QWjm2GBcyH3NpgbfJoxzKgv1LKOWvooTaw05iU+XIGaJ213Q44AqCUclVKlc3a7gCka60PGBPxnu7aBjKHTdqpTGWBB4BDBuTLr9zakf3v7FFgfomlMfod7ZZ3rtFk9k4PA+/z97vcYDK/9NsD7AYeNjprIdvxOpljfXtuuVU0Om9B2pD1WCxwEbhO5tixRfb48tGO18g8+ySGrLMKLPVG5pk/u8gc8/4daJy1PyAr/0FgHZlTjhqet4BtcAMWZv0fPwCMNzprYdqR9VgbYEdJ5pFL6YUQwkpZzBCKEEKIgpECLoQQVkoKuBBCWCkp4EIIYaWkgAshhJWSAi6EEFZKCrgQQlip/wfZnQB0Ry4FrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11855145348562784 = WI std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12450441173542125 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for WI\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.7856\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00214 0.49895 HD avg vs true 0.49681\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.06302, should have been 0.06802\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for WI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the GA map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  736714.375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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A3iTLwPur6s5xU23aNcA7gC8P+3EA3ldVnx4v0pbsB44ORzK9CDhWVTN3uPaMugS4d/V3HPYAH62qz4wbaUt+C/jI8Evx14DfHDnPliR5CatHPr9r7CwvZOYPM5ckzad5WOKTJM0hC0qS1JIFJUlqyYKSJLVkQUmSWrKgJEktWVCSpJb+Bx4Tg+6D5auXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.469 2.991\n",
      "fractional expected GOP seats =  4.667  out of  8 seats for WI\n",
      "simpler expected GOP seats =  4.8035  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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